Which risk‐adjustment index performs better in predicting 30‐day mortality? A systematic review and meta‐analysis

Yang, Mo; Mehta, Hemalkumar B.; Bali, Vishal; Gupta, Parul Chawla; Wang, Xin; Johnson, Michael L.; Aparasu, Rajender R.

doi:10.1111/jep.12307

Cited by 12 publications

(7 citation statements)

References 55 publications

(68 reference statements)

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“…Based on "PubMed" database search using terms "in-hospital mortality," "in-hospital deaths," and "inter-hospital transfers" and after review of other mortality prediction models, we identified 30 putative variables (see Table 1) for inclusion in the mortality model. 5,14,15,18,26 Demographic data included age and gender. Admit type was defined as an emergency, urgent, and routine.…”

Section: Variablesmentioning

confidence: 99%

“…4,11,12 Recognizing these complexities clinicians have expressed an interest in adopting evidencebased clinical prediction models to increase their prognostic confidence in the end-of-life care. 13 Available models predicting mortality are often limited to ICU settings, 14 are condition-specific, [15][16][17][18] or predict deaths after hospital discharge. [19][20][21] Multiple early warning systems (EWS) have been developed that use vital sign abnormalities prior to clinical deterioration with efforts to predict in-hospital mortality, but these models are limited by poor sensitivity, poor positive predictive value, and low reproducibility.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

et al. 2021

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BACKGROUND: Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients. OBJECTIVE: Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC). DESIGN: Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195). PARTICIPANTS: Ten thousand three hundred eightynine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017. MAIN MEASURES: Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model. KEY RESULTS: The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X 2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of −2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold. CONCLUSION: This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

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Section: Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

et al. 2021

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“…However, despite a great amount of evidence on short-term prognosis is available, only a few studies investigated the predictors of mortality beyond the first 30 days after acute PE [ 8 , 9 , 10 ], and in the elderly the prognostic value of clinical prediction rules and comorbidities burden has not been clearly established. The use of the sPESI score as may be interesting due to its simplicity and widespread use, and comorbidities may have a significant impact on long-term mortality [ 11 , 12 ], especially in elderly subjects who are likely to have a higher comorbidity burden [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Predictors of medium- and long-term mortality in elderly patients with acute pulmonary embolism

Friz

Orenti

Gelfi

et al. 2020

Heliyon

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Introduction Data on medium- and long-term prognostic factors for death in elderly patients with acute Pulmonary Embolism (APE) are lacking. The present study aimed to assess sPESI score and the Charlson Comorbidity Index (CCI) as medium- and long-term predictors of mortality in elderly patients with haemodinamically stable APE. Methods All consecutive patients aged≥65 years old, evaluated at the emergency department (ED) of our hospital from 2010 through 2014, with a final diagnosis of APE, were included in this retrospective cohort study. Results Study population:162 patients, female:36.5%, median age:79 years old, 74% presented a sPESI score>0, and 61% a CCI≥ 1. All causes mortality: 19.8%, 23.5%, 26.5%, 32.1% and 48.2% at 3, 6 months, 1, 2 and 5 years after APE. Univariate regression analysis: CCI≥1 was associated with a higher mortality at 3, 6 months, 1, 2 and 5 years. Multivariate Cox analysis: CCI≥1 associated with increased mortality at 3 months (HR:4.29; IC95%:1.46–12.59), 6 months (HR:5.33; IC95%:1.84–15.44), 1 year (HR:4.87; IC95%:1.87–12.70), 2 years (HR:3.78; IC95%:1.74–8.25), and 5 years (HR:2.30; IC95%:1.33–3.99). sPESI score≥1 was not found to be related to an increased medium-or long-term mortality. Negative predictive values (IC95%) of CCI≥1 were 93.65% (87.61–99.69), 93.65% (87.61–99.69), 92.06% (85.37–98.76), 87.3% (79.05–95.55) and 71.61% (60.13–83.1) for mortality at 3, 6 months, 1, 2 and 5 years. Conclusion In elderly patients with a confirmed normotensive APE, unlike sPESI score, CCI showed to be an independent prognostic factor for medium- and long-term mortality. In these patients, after the acute phase following a PE event, the assessment of the comorbidities burden represents the most appropriate approach for predicting medium- and long-term mortality.

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“…A uniform limitation of many administrative database analyses is the lack of patient-level severity of illness scoring. However, comorbidity-based risk-adjustment indices (e.g., Charlson index AUROC = 0.67) have been shown to perform similarly well as severity of illness scores (APACHE II AUROC = 0.805 and SAPS II AUROC = 0.843) in predicting short-term mortality following ICU admission [18][19][20]. The discriminative ability of our comorbidity-based mortality model, while still dependent on ICD-9-CM-based coding instead of physiologic variables, compares favorably (AUROC = 0.80).…”

Section: Discussionmentioning

confidence: 99%

Variability in usual care fluid resuscitation and risk-adjusted outcomes for mechanically ventilated patients in shock

Mansoori

Linde-Zwirble²,

Hou

et al. 2020

Crit Care

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Rationale: There remains significant controversy regarding the optimal approach to fluid resuscitation for patients in shock. The magnitude of care variability in shock resuscitation, the confounding effects of disease severity and comorbidity, and the relative impact on sepsis survival are poorly understood. Objective: To evaluate usual care variability and determine the differential effect of observed and predicted fluid resuscitation volumes on risk-adjusted hospital mortality for mechanically ventilated patients in shock. Methods: We performed a retrospective outcome analysis of mechanically ventilated patients admitted to intensive care units using the 2013 Premier Hospital Database (Premier, Inc.). Observed and predicted hospital mortality were evaluated by observed and predicted day 1 fluid administration, using the difference in predicted and observed outcomes to adjust for disease severity between groups. Both predictive models were validated using a second large administrative database (Truven Health Analytics Inc.). Secondary outcomes included duration of mechanical ventilation, hospital and ICU length of stay, and cost. Results: Among 33,831 patients, observed hospital mortality was incrementally higher than predicted for each additional liter of day 1 fluid beginning at 7 L (40.9% vs. 37.2%, p = 0.008). Compared to patients that received expected (± 1.5 L predicted) day 1 fluid volumes, greater-than-expected fluid resuscitation was associated with increased risk-adjusted hospital mortality (52.3% vs. 45.0%, p < 0.0001) among all patients with shock and among a subgroup of shock patients with comorbid conditions predictive of lower fluid volume administration (47.1% vs. 41.5%, p < 0.0001). However, in patients with shock but without such conditions, both greater-than-expected (57.5% vs. 49.2%, p < 0.0001) and less-than-expected (52.1% vs. 49.2%, p = 0.037) day 1 fluid resuscitation were associated with increased risk-adjusted hospital mortality. Conclusions: Highly variable day 1 fluid resuscitation was associated with a non-uniform impact on risk-adjusted hospital mortality among distinct subgroups of mechanically ventilated patients with shock. These findings support closer evaluation of fluid resuscitation strategies that include broadly applied fluid volume targets in the early phase of shock resuscitation.

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Which risk‐adjustment index performs better in predicting 30‐day mortality? A systematic review and meta‐analysis

Abstract: Although all the selected risk-adjustment indices perform equally well, SAPS seems better than other indices for short-term mortality based on scaled ranking score.

Cited by 12 publications

References 55 publications

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

Predictors of medium- and long-term mortality in elderly patients with acute pulmonary embolism

Variability in usual care fluid resuscitation and risk-adjusted outcomes for mechanically ventilated patients in shock

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