External validation of a modified model of Acute Physiology and Chronic Health Evaluation (APACHE) II for orthotopic liver transplant patients

Post liver transplantation delirium assessment using the CAM-ICU-7 scale: A cohort analysis

Background

We applied the Confusion Assessment Method (CAM)-Intensive Care Unit (ICU)-7 delirium scale to patients who underwent liver transplant (LT).

Methods

Retrospective cohort including patients who underwent LT for cirrhosis admitted to the ICU from June 2013 to June 2016 at the University of Alberta Hospital, Canada. Delirium was assessed using the CAM-ICU-7 scale (0-7 points) twice daily on days one and 3 post LT, with the highest score being considered. Primary endpoint was hospital mortality.

Results

Among all patients, 101/150 (67.3%) were men and mean age was 52.4 (SD 11.8) years. On days 1 and 3 post LT, mean CAM-ICU-7 scores were 1.8 (SD 1.3) and 1.6 (SD 1.8), respectively. Therefore, on days 1 and 3 post LT, 38/150 (25.3%) and 26/95 (27.4%) patients had delirium. While delirium on day 3 post LT was associated with higher hospital mortality (11.5% versus 0%; p = 0.019), it was not associated with length-of-hospital stay (29.2 versus 34.4 days; p = 0.36). Following adjustment for APACHEII score, delirium on day 3 post LT was associated with higher odds of hospital mortality (adjusted odds ratio [aOR] 1.89 [95% CI 1.02-3.50]). Following adjustment for Glasgow Coma Scale and mechanical ventilation, serum creatinine was associated with higher odds of delirium on day 3 post LT (aOR 2.02 [95% CI 1.08-3.77]).

Conclusions

Using the CAM-ICU-7 scale, delirium was diagnosed in a fourth of patients who underwent LT. Delirium on day 3 post LT was associated with higher odds of hospital mortality.

Trauma and nontrauma damage-control laparotomy: The difference is delirium (data from the Eastern Association for the Surgery of Trauma SLEEP-TIME multicenter trial)

BACKGROUND

Damage-control laparotomy (DCL) has been used for traumatic and nontraumatic indications. We studied factors associated with delirium and outcome in this population.

METHODS

We reviewed DCL patients at 15 centers for 2 years, including demographics, Charlson Comorbidity Index (CCI), diagnosis, operations, and outcomes. We compared 30-day mortality; renal failure requiring dialysis; number of takebacks; hospital, ventilator, and intensive care unit (ICU) days; and delirium-free and coma-free proportion of the first 30 ICU days (DF/CF-ICU-30) between trauma (T) and nontrauma (NT) patients. We performed linear regression for DF/CF-ICU-30, including age, sex, CCI, achievement of primary fascial closure (PFC), small and large bowel resection, bowel discontinuity, abdominal vascular procedures, and trauma as covariates. We performed one-way analysis of variance for DF/CF-ICU-30 against traumatic brain injury severity as measured by Abbreviated Injury Scale for the head.

RESULTS

Among 554 DCL patients (25.8% NT), NT patients were older (58.9 ± 15.8 vs. 39.7 ± 17.0 years, p < 0.001), more female (45.5% vs. 22.1%, p < 0.001), and had higher CCI (4.7 ± 3.3 vs. 1.1 ± 2.2, p < 0.001). The number of takebacks (1.7 ± 2.6 vs. 1.5 ± 1.2), time to first takeback (32.0 hours), duration of bowel discontinuity (47.0 hours), and time to PFC were similar (63.2 hours, achieved in 73.5%). Nontrauma and T patients had similar ventilator, ICU, and hospital days and mortality (31.0% NT, 29.8% T). Nontrauma patients had higher rates of renal failure requiring dialysis (36.6% vs. 14.1%, p < 0.001) and postoperative abdominal sepsis (40.1% vs. 17.1%, p < 0.001). Trauma and NT patients had similar number of hours of sedative (89.9 vs. 65.5 hours, p = 0.064) and opioid infusions (106.9 vs. 96.7 hours, p = 0.514), but T had lower DF/CF-ICU-30 (51.1% vs. 73.7%, p = 0.029), indicating more delirium. Linear regression analysis indicated that T was associated with a 32.1% decrease (95% CI, 14.6%-49.5%; p < 0.001) in DF/CF-ICU-30, while achieving PFC was associated with a 25.1% increase (95% CI, 10.2%-40.1%; p = 0.001) in DF/CFICU-30. Increasing Abbreviated Injury Scale for the head was associated with decreased DF/CF-ICU-30 by analysis of variance (p < 0.001).

CONCLUSION

Nontrauma patients had higher incidence of postoperative abdominal sepsis and need for dialysis, while T was independently associated with increased delirium, perhaps because of traumatic brain injury.

LEVEL OF EVIDENCE

Therapeutic study, level IV.

[Validation of APACHE IV score in postoperative liver transplantation in southern Brazil: a cohort study]

BACKGROUND

Liver transplantation is the only curative therapeutic modality available for individuals at end-stage liver disease. There is no reliable method of predicting the early postoperative outcome of these patients. The Acute Physiology and Chronic Health Evaluation (APACHE) is a widely used model for predicting hospital survival and benchmarking in critically ill patients. This study evaluated the calibration and discrimination of APACHE IV in the postoperative period of elective liver transplantation in the southern Brazil.

METHODS

This was a clinical prospective and unicentric cohort study that included 371 adult patients in the immediate postoperative period of elective liver transplantation from January 1, 2012 to December 31, 2016.

RESULTS

In this study, liver transplant patients who evolved to hospital death had a significantly higher APACHE IV score (82.7±5.1 vs. 51.0±15.8; p<0.001) and higher predicted mortality (6.5% [4.4-20.2%] vs. 2.3% [1.4-3.5%]; p<0.001). The APACHE IV score showed an adequate calibration (Hosmer-Lemeshow - H-L=11.37; p=0.181) and good discrimination (Receiver Operator Curve - ROC of 0.797; Confidence Interval 95% - 95% CI 0.713-0.881; p<0.0001), although Standardized Mortality Ratio (SMR=2.63), (95% CI 1.66-4.27; p<0.001) underestimate mortality.

CONCLUSIONS

In summary, the APACHE IV score showed an acceptable performance for predicting a hospital outcome in the postoperative period of elective liver transplant recipients.

Simplified prognostic model for critically ill patients in resource limited settings in South Asia

BACKGROUND

Current critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission.

METHODS

This was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures. Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model. Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models.

RESULTS

Model 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence interval (CI) 0.800-0.835) for model 1, 0.767 (0.741-0.792) for TropICS, and 0.725 (0.688-0.762) for model 3. The Hosmer-Lemeshow C-Statistic p values were less than 0.05 for models 1 and 3 and 0.18 for TropICS. In comparison, when APACHE II and SAPS II were applied to the same dataset, AUC was 0.707 (0.688-0.726) and 0.714 (0.695-0.732) and the C-Statistic was 124.84 (p < 0.001) and 1692.14 (p < 0.001), respectively.

CONCLUSION

This paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting.

Comparison of APACHE IV with APACHE II, SAPS 3, MELD, MELD-Na, and CTP scores in predicting mortality after liver transplantation

The Acute Physiology and Chronic Health Evaluation (APACHE) IV score and Simplified Acute Physiology Score (SAPS) 3 include liver transplantation as a diagnostic category. The performance of APACHE IV-liver transplantation (LT) specific predicted mortality, SAPS 3, APACHE II, Model for End-stage Liver Disease (MELD)-Na, MELD, and CTP scores in predicting in-hospital and 1 year mortality in liver transplant patients was compared using 590 liver transplantations in a single university hospital. In-hospital mortality and 1 year mortality were 2.9% and 4.2%, respectively. The APACHE IV-LT specific predicted mortality showed better performance in predicting in-hospital mortality (AUC 0.91, 95% CI [0.86–0.96]) compared to SAPS 3 (AUC 0.78, 95% CI [0.66–0.90], p = 0.01), MELD-Na (AUC 0.74, 95% CI [0.57–0.86], p = 0.01), and CTP (AUC 0.68, 95% CI [0.54–0.81], p = 0.01). The APACHE IV-LT specific predicted mortality showed better performance in predicting 1 year mortality (AUC 0.83, 95% CI [0.76–0.9]) compared to MELD-Na (AUC 0.67, 95% CI [0.55–0.79], p = 0.04) and CTP (AUC 0.64, 95% CI [0.53–0.75], p = 0.03), and also in all MELD groups and in both living and deceased donor transplantation. The APACHE IV-LT specific predicted mortality showed better performance in predicting in-hospital and 1 year mortality after liver transplantation.

Applicability of the APACHE II model to a lower middle income country

PURPOSE

To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data.

MATERIALS AND METHODS

Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE).

RESULTS

From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively.

CONCLUSIONS

An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted.

Prediction of Outcome From Adult Bacterial Meningitis in a High-HIV-Seroprevalence, Resource-Poor Setting Using the Malawi Adult Meningitis Score (MAMS)

Background.

Acute bacterial meningitis (ABM) in adults residing in resource-poor countries is associated with mortality rates >50%. To improve outcome, interventional trials and standardized clinical algorithms are urgently required. To optimize these processes, we developed and validated an outcome prediction tool to identify ABM patients at greatest risk of death.

Methods.

We derived a nomogram using mortality predictors derived from a logistic regression model of a discovery database of adult Malawian patients with ABM (n = 523 [65%] cerebrospinal fluid [CSF] culture positive). We validated the nomogram internally using a bootstrap procedure and subsequently used the nomogram scores to further interpret the effects of adjunctive dexamethasone and glycerol using clinical trial data from Malawi.

Results.

ABM mortality at 6-week follow-up was 54%. Five of 15 variables tested were strongly associated with poor outcome (CSF culture positivity, CSF white blood cell count, hemoglobin, Glasgow Coma Scale, and pulse rate), and were used in the derivation of the Malawi Adult Meningitis Score (MAMS) nomogram. The C-index (area under the curve) was 0.76 (95% confidence interval, .71–.80) and calibration was good (Hosmer-Lemeshow C-statistic = 5.48, df = 8, P = .705). Harmful effects of adjunctive glycerol were observed in groups with relatively low predicted risk of poor outcome (25%–50% risk): Case Fatality Rate of 21% in the placebo group and 52% in the glycerol group (P < .001). This effect was not seen with adjunctive dexamethasone.

Conclusions.

MAMS provides a novel tool for predicting prognosis and improving interpretation of ABM clinical trials by risk stratification in resource-poor settings. Whether MAMS can be applied to non-HIV-endemic countries requires further evaluation.

APACHE IV is superior to MELD scoring system in predicting prognosis in patients after orthotopic liver transplantation

This study aims to compare the efficiency of APACHE IV with that of MELD scoring system for prediction of the risk of mortality risk after orthotopic liver transplantation (OLT). A retrospective cohort study was performed based on a total of 195 patients admitted to the ICU after orthotopic liver transplantation (OLT) between February 2006 and July 2009 in Guangzhou, China. APACHE IV and MELD scoring systems were used to predict the postoperative mortality after OLT. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow C statistic were used to assess the discrimination and calibration of APACHE IV and MELD, respectively. Twenty-seven patients died during hospitalization with a mortality rate of 13.8%. The mean scores of APACHE IV and MELD were 42.32 ± 21.95 and 18.09 ± 10.55, respectively, and APACHE IV showed better discrimination than MELD; the areas under the receiver operating characteristic curve for APACHE IV and MELD were 0.937 and 0.694 (P < 0.05 for both models), which indicated that the prognostic value of APACHE IV was relatively high. Both models were well-calibrated (The Hosmer-Lemeshow C statistics were 1.568 and 6.818 for APACHE IV and MELD, resp.; P > 0.05 for both). The respective Youden indexes of APACHE IV, MELD, and combination of APACHE IV with MELD were 0.763, 0.430, and 0.545. The prognostic value of APACHE IV is high but still underestimates the overall hospital mortality, while the prognostic value of MELD is poor. The function of the APACHE IV is, thus, better than that of the MELD.

Is SAPS 3 better than APACHE II at predicting mortality in critically ill transplant patients?

OBJECTIVES

This study compared the accuracy of the Simplified Acute Physiology Score 3 with that of Acute Physiology and Chronic Health Evaluation II at predicting hospital mortality in patients from a transplant intensive care unit.

METHOD

A total of 501 patients were enrolled in the study (152 liver transplants, 271 kidney transplants, 54 lung transplants, 24 kidney-pancreas transplants) between May 2006 and January 2007. The Simplified Acute Physiology Score 3 was calculated using the global equation (customized for South America) and the Acute Physiology and Chronic Health Evaluation II score; the scores were calculated within 24 hours of admission. A receiver-operating characteristic curve was generated, and the area under the receiver-operating characteristic curve was calculated to identify the patients at the greatest risk of death according to Simplified Acute Physiology Score 3 and Acute Physiology and Chronic Health Evaluation II scores. The Hosmer-Lemeshow goodness-of-fit test was used for statistically significant results and indicated a difference in performance over deciles. The standardized mortality ratio was used to estimate the overall model performance.

RESULTS

The ability of both scores to predict hospital mortality was poor in the liver and renal transplant groups and average in the lung transplant group (area under the receiver-operating characteristic curve = 0.696 for Simplified Acute Physiology Score 3 and 0.670 for Acute Physiology and Chronic Health Evaluation II). The calibration of both scores was poor, even after customizing the Simplified Acute Physiology Score 3 score for South America.

CONCLUSIONS

The low predictive accuracy of the Simplified Acute Physiology Score 3 and Acute Physiology and Chronic Health Evaluation II scores does not warrant the use of these scores in critically ill transplant patients.

APACHE III score on ICU admission predicts hospital mortality after open thoracoabdominal and open abdominal aortic aneurysm repair

BACKGROUND

No prior studies, to our knowledge, have examined the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) III score in predicting mortality of patients undergoing open thoracoabdominal aortic aneurysm (TAAA) or open abdominal aortic aneurysm (AAA) repair. We sought to evaluate APACHE III scores in the prediction of postoperative mortality in elective TAAA and AAA repairs.

METHODS

Over a 9-year period (July 1998 through June 2007), prospective data (demographics, admitting diagnosis, APACHE III score, intensive care unit [ICU] and hospital length of stay, ICU and hospital mortality) were collected by a dedicated APACHE III coordinator for all patients admitted to a tertiary academic surgical ICU (20 beds). Observational and comparative analyses were performed. Emergent repairs for ruptured aneurysms were excluded from the study.

RESULTS

Forty-one patients underwent open elective repair of TAAA and 404 underwent open elective repair of AAA. Mean age of the TAAA group was 63.4 ± 9.8 years and the AAA group was 70.3 ± 8.3 years. Mean APACHE III score was 54 (range: 10-103) for the TAAA group and 45 (range: 11-103) for the AAA group. The in-hospital mortality rate for TAAA patients was 4.9% (n = 2) and for AAA patients was 2.0% (n = 8). Mean APACHE III scores on ICU admission were significantly greater in nonsurvivors versus survivors (79 vs. 45, p < 0.0001). For the entire patient cohort, the APACHE III score on ICU admission was an excellent discriminator of hospital mortality (receiver operating characteristic and area under the curve 0.92 [standard error of 0.05, 95% CI: 0.83-1.0]).

CONCLUSIONS

APACHE III is an accurate predictor of survival to hospital discharge in both open elective TAAA and AAA repairs.

APACHE III outcome prediction in patients admitted to the intensive care unit after liver transplantation: a retrospective cohort study

Background

The Acute Physiology and Chronic Health Evaluation (APACHE) III prognostic system has not been previously validated in patients admitted to the intensive care unit (ICU) after orthotopic liver transplantation (OLT). We hypothesized that APACHE III would perform satisfactorily in patients after OLT

Methods

A retrospective cohort study was performed. Patients admitted to the ICU after OLT between July 1996 and May 2008 were identified. Data were abstracted from the institutional APACHE III and liver transplantation databases and individual patient medical records. Standardized mortality ratios (with 95% confidence intervals) were calculated by dividing the observed mortality rates by the rates predicted by APACHE III. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow C statistic were used to assess, respectively, discrimination and calibration of APACHE III.

Results

APACHE III data were available for 918 admissions after OLT. Mean (standard deviation [SD]) APACHE III (APIII) and Acute Physiology (APS) scores on the day of transplant were 60.5 (25.8) and 50.8 (23.6), respectively. Mean (SD) predicted ICU and hospital mortality rates were 7.3% (15.4) and 10.6% (18.9), respectively. The observed ICU and hospital mortality rates were 1.1% and 3.4%, respectively. The standardized ICU and hospital mortality ratios with their 95% C.I. were 0.15 (0.07 to 0.27) and 0.32 (0.22 to 0.45), respectively.

There were statistically significant differences in APS, APIII, predicted ICU and predicted hospital mortality between survivors and non-survivors. In predicting mortality, the AUC of APACHE III prediction of hospital death was 0.65 (95% CI, 0.62 to 0.68). The Hosmer-Lemeshow C statistic was 5.288 with a p value of 0.871 (10 degrees of freedom).

Conclusion

APACHE III discriminates poorly between survivors and non-survivors of patients admitted to the ICU after OLT. Though APACHE III has been shown to be valid in heterogenous populations and in certain groups of patients with specific diagnoses, it should be used with caution – if used at all – in recipients of liver transplantation.

Readmission to Surgical Intensive Care Increases Severity-Adjusted Patient Mortality

BACKGROUND

This study aims to determine whether severity-adjusted outcomes including mortality are adversely impacted by readmission to a surgical intensive care unit (SICU) during the same hospital stay.

METHODS

The study included all patients admitted to the 20-bed tertiary care SICU in an urban teaching Level I trauma center and multiorgan transplant center from January 1, 1996 to December 31, 2001. This was a prospective observational study with secondary data analysis. Acute Physiology and Chronic Health Evaluation (APACHE II) and Simplified Acute Physiology (SAPS) severity scores were calculated by a clinical information system. Outcomes were extracted from a computerized data warehouse.

RESULTS

In-hospital mortality and SICU length of stay (LOS) were measured for patients admitted and readmitted to the SICU. Of 10,840 patients admitted to the SICU, 296 (2.73%) required readmission to the SICU during the same hospital stay. The length of the original SICU stay was 4.9 +/- 6.7 days for readmitted patients compared with 3.2 +/- 6.0 days for nonreadmitted patients (p < 0.001). Readmitted patients had a higher mean APACHE II score on the day of original SICU discharge compared with nonreadmitted patients, 15.7 +/- 6.7 versus 13.8 +/- 7.1 (p < 0.001). The average APACHE II score increased from 15.7 +/- 6.7 to 18.1 +/- 8.6 between the day of SICU discharge and readmission (p < 0.001) and SAPS increased from 12.2 +/- 4.8 to 13.5 +/- 5.4 (p < 0.001). The distributions of severity-adjusted hospital mortality for both APACHE II and SAPS revealed that readmission to the SICU significantly increased mortality independent of the admission severity score.

CONCLUSIONS

Readmission to the SICU significantly increases the risk of death beyond that predicted by the APACHE II or SAPS scores alone. Higher APACHE II and SAPS scores upon discharge from the SICU and longer SICU LOS are associated with an increased incidence of readmission to the SICU on the same hospital stay. These results may be used to optimize the timing of SICU discharge and reduce the chance of readmission to intensive care.

Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

BACKGROUND

There is a relative lack of donor organs for liver transplantation. Ideally, to maximize the utility of those livers that are offered, donor and recipient characteristics should be matched to ensure the best possible posttransplant survival of the recipient.

METHODS

With prospectively collected data on 827 patients receiving a primary liver graft for chronic liver disease, we used a self-organizing map (SOM) (one form of a neural network) to predict outcome after transplantation using both donor and recipient factors. The SOM was then validated using a data set of 2622 patients undergoing transplantation in the United Kingdom at other centers.

RESULTS

SOM analysis using 72 inputs and two survival intervals (3 and 12 months) yielded three neurons with either higher or lower probabilities of survival. The model was validated using the independent data set. With 20 patients on the waiting list and 10 sequential donor livers, it was possible to demonstrate that the model could be used to identify which potential recipients were likely to benefit most from each liver offered.

CONCLUSIONS

With this approach to matching donor livers and recipients, it is possible to inform transplant clinicians about the optimum use of donor livers and thereby effectively make the best use of a scarce resource.

The customization of APACHE II for patients receiving orthotopic liver transplants

General outcome prediction models developed for use with large, multicenter databases of critically ill patients may not correctly estimate mortality if applied to a particular group of patients that was under-represented in the original database. The development of new diagnostic weights has been proposed as a method of adapting the general model - the Acute Physiology and Chronic Health Evaluation (APACHE) II in this case - to a new group of patients. Such customization must be empirically tested, because the original model cannot contain an appropriate set of predictive variables for the particular group. In this issue of Critical Care, Arabi and co-workers present the results of the validation of a modified model of the APACHE II system for patients receiving orthotopic liver transplants. The use of a highly heterogeneous database for which not all important variables were taken into account and of a sample too small to use the Hosmer-Lemeshow goodness-of-fit test appropriately makes their conclusions uncertain.