External validation of the Acute Physiology and Chronic Health Evaluation II in Korean intensive care units

Predictive Mortality of the Prognostic Nutritional Index Combined with APACHE II Score for Critically Ill Tuberculosis Patients

High mortality rates are commonly found in critically ill patients with tuberculosis (TB), which is due partially to limitations in the existing prognostic evaluation methods. Therefore, we aimed to find more effective prognostic evaluation tools to reduce the mortality rate. Data from critically ill patients with TB admitted to the intensive care unit of The Second Hospital of Nanjing, Nanjing, China, between January 2020 and December 2022 were analyzed retrospectively. A total of 115 patients were enrolled and divided into a survival group (n = 62) and a death group (n = 53) according to 30-day survival. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to investigate the risk factors for 30-day death in critically ill patients with TB. A prediction model for risk of 30-day mortality was developed for critically ill patients with TB in the intensive care unit. The LASSO regression model showed that the prognostic nutritional index (PNI) and Acute Physiology and Chronic Health Status (APACHE II) scores on the third day after admission to the intensive care unit were independent risk factors for 30-day mortality in critically ill patients with TB (P <0.05). The area under the curve value and that PA3 represents the combination of the PNI and APACHE II score on the third day, which was 0.952 (95% CI: 0.913-0.991, P <0.001), was significantly higher than that of the PNI or the APACHE II score on the third day. The new model is as follows: PA3 = APACHE II score (on the third day) × 0.421 - PNI × 0.204. The PNI combined with the APACHE II score on the third day could well predict the 30-day mortality risk of critically ill patients with TB.

Development and validation of novel simple prognostic model for predicting mortality in Korean intensive care units using national insurance claims data

BACKGROUND/AIMS

Intensive care unit (ICU) quality is largely determined by the mortality rate. Therefore, we aimed to develop and validate a novel prognostic model for predicting mortality in Korean ICUs, using national insurance claims data.

METHODS

Data were obtained from the health insurance claims database maintained by the Health Insurance Review and Assessment Service of South Korea. From patients who underwent the third ICU adequacy evaluation, 42,489 cases were enrolled and randomly divided into the derivation and validation cohorts. Using the models derived from the derivation cohort, we analyzed whether they accurately predicted death in the validation cohort. The models were verified using data from one general and two tertiary hospitals.

RESULTS

Two severity correction models were created from the derivation cohort data, by applying variables selected through statistical analysis, through clinical consensus, and from performing multiple logistic regression analysis. Model 1 included six categorical variables (age, sex, Charlson comorbidity index, ventilator use, hemodialysis or continuous renal replacement therapy, and vasopressor use). Model 2 additionally included presence/absence of ICU specialists and nursing grades. In external validation, the performance of models 1 and 2 for predicting in-hospital and ICU mortality was not inferior to that of pre-existing scoring systems.

CONCLUSION

The novel and simple models could predict in-hospital and ICU mortality and were not inferior compared to the pre-existing scoring systems.

Association of body temperature and mortality in critically ill patients: an observational study using two large databases

BACKGROUND

Body temperature (BT) is routinely measured and can be controlled in critical care settings. BT can impact patient outcome, but the relationship between BT and mortality has not been well-established.

METHODS

A retrospective cohort study was conducted based on the MIMIC-IV (N = 43,537) and eICU (N = 75,184) datasets. The primary outcome and exposure variables were hospital mortality and first 48-h median BT, respectively. Generalized additive models were used to model the associations between exposures and outcomes, while adjusting for patient age, sex, APS-III, SOFA, and Charlson comorbidity scores, temperature gap, as well as ventilation, vasopressor, steroids, and dialysis usage. We conducted subgroup analysis according to ICU setting, diagnoses, and demographics.

RESULTS

Optimal BT was 37 °C for the general ICU and subgroup populations. A 10% increase in the proportion of time that BT was within the 36-38 °C range was associated with reduced hospital mortality risk in both MIMIC-IV (OR 0.91; 95% CI 0.90-0.93) and eICU (OR 0.86; 95% CI 0.85-0.87). On the other hand, a 10% increase in the proportion of time when BT < 36 °C was associated with increased mortality risk in both MIMIC-IV (OR 1.08; 95% CI 1.06-1.10) and eICU (OR 1.18; 95% CI 1.16-1.19). Similarly, a 10% increase in the proportion of time when BT > 38 °C was associated with increased mortality risk in both MIMIC-IV (OR 1.09; 95% CI 1.07-1.12) and eICU (OR 1.09; 95% CI 1.08-1.11). All patient subgroups tested consistently showed an optimal temperature within the 36-38 °C range.

CONCLUSIONS

A BT of 37 °C is associated with the lowest mortality risk among ICU patients. Further studies to explore the causal relationship between the optimal BT and mortality should be conducted and may help with establishing guidelines for active BT management in critical care settings.

Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients

Objective

This study aims to evaluate the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II score on different days in predicting the mortality of critically ill patients to identify the best time point for the APACHE II score.

Methods

The demographic and clinical data are retrieved from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APACHE II scores on days 1, 2, 3, 5, 7, 14, and 28 of hospitalization are calculated, and their performance is evaluated using the area under the receiver operating characteristic (AUROC) analysis. The cut-off for defining the high risk of mortality is determined using Youden's index. The APACHE II score on day 3 is the best time point to predict hospital mortality of ICU patients. The Hosmer-Lemeshow goodness-of-fit test is then applied to evaluate the calibration of the day 3 APACHE II score.

Results

We recruited 6,374 eligible subjects from the MIMIC-IV database. Day 3 is the optimal time point for obtaining the APACHE II score to predict the hospital mortality of patients. The best cut-off for day 3 APACHE II score is 17. When APACHE II score ≥17, the sensitivity for the non-survivors and survivors is 92.8 and 82.2%, respectively, and the positive predictive value (PPV) is 23.1%. When APACHE II socre <17, the specificity for non-survivors and survivors is 90.1 and 80.2%, respectively, and the negative predictive value (NPV) is 87.8%. When day-3 APACHE II is used to predict the hospital mortality, the AUROC is 0.743 (P <0.001). In the ≥17 group, the sensitivity of non-survivors and survivors is 92.2 and 81.3%, respectively, and the PPV is 30.3%. In the <17 group, the specificity of non-survivors and survivors is 100.0 and 80.2%, respectively, and the NPV is 81.6%. The Hosmer-Lemeshow test indicated day-3 APACHE II has a high predicting the hospital mortality (X² = 6.198, P = 0.625, consistency = 79.4%). However, the day-1 APACHE II has a poor calibration in predicting the hospital mortality rate (X² = 294.898, P <0.001).

Conclusion

Day-3 APACHE II score is an optimal biomarker to predict the outcomes of ICU patients; 17 is the best cut-off for defining patients at high risk of mortality.

Acute Physiology and Chronic Health Evaluation II score for the assessment of mortality prediction in the intensive care unit: a single-centre study from Iran

BACKGROUND

The Acute Physiology and Chronic Health Evaluation (APACHE) II is still commonly used as an index of illness severity in patients admitted to intensive care unit (ICU) and has been validated for many research and clinical audit purposes.

AIMS AND OBJECTIVES

To investigate the diagnostic value of the APACHE II score for predicting mortality rate of critically ill patients.

DESIGN

This was a single-centre, retrospective study of 200 Iranian patients admitted in the medical-surgical adult ICU from June 2012 to May 2013.

METHODS

Demographic data, pre-existing comorbidities and variables required for calculating the APACHE II score were recorded. Receiver operating characteristic (ROC) curves were constructed, and the area under the ROC curves was calculated to assess the predictive value of the APACHE II score.

RESULTS

Of the 200 patients with a mean age of 55·27 ± 21·59 years enrolled in the study, 112 (54%) were admitted in the medical ICU and 88 (46%) in the surgical ICU. Finally, 116 patients (58%) died, and 84 patients (42%) survived. The overall actual and predicted ICU mortality were 58% and 25·16%, respectively. The mean APACHE II score was 16·31 in total patients, 17·78 in medical ICU and 14·45 in surgical ICU patients (P = 0·003). Overall, the APACHE II score had the highest prognostic value for predicting the mortality rate of critically ill patients with an area under the cure of 0·88, and with a cut-off value of 15, the APACHE II score predicted mortality of patients with a sensitivity of 85·3%, a specificity of 77·4%, a positive predictive value of 83·9% and a negative predictive value of 73·9%.

CONCLUSION

This study shows that an APACHE II score of 15 provides the best diagnostic accuracy to predict mortality of critically ill patients. Our observed mortality rate was greater than the predicted death rate, in comparison to the other prestigious centres in the world. Therefore, it appears that we must improve our intensive care to reduce mortality.

RELEVANCE TO CLINICAL PRACTICE

There is a need to create a suitable scoring system to predict the mortality rate of critically ill patients in accordance with the advanced technological equipment and experienced physicians and nurses in that ICU.

Performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) in the prediction of hospital mortality in a mixed ICU in Singapore

Background:

The Acute Physiology and Chronic Health Evaluation II (APACHE II) is used to quantify disease severity and hospital mortality risk in critically ill patients. It is widely used in intensive care units (ICUs) in Singapore, but its prognostic validity remains questionable as it has not been thoroughly assessed by established statistical methods.

Objectives:

This study aimed to: (a) evaluate the discrimination and calibration accuracy of the APACHE II in the prediction of hospital mortality in a mixed ICU, and (b) customise the APACHE II in an effort to maximise its prognostic performance.

Methods:

A prospective cohort study was conducted and all adult patients with >24 h of ICU admission in a tertiary care institution in Singapore were included. The outcome measure was hospital mortality, and all patients were followed-up until hospital discharge or death for up to one year after ICU admission.

Results:

There were 503 patients, and their mean (SD) age and APACHE II score were 61.2 (15.8) years and 24.5 (8.2), respectively. Hospital mortality was 31%, and no patients were lost to follow-up. The APACHE II has good discrimination (receiver operating characteristic: 0.76) but poor calibration (Hosmer–Lemeshow C test: <0.001). Customisation did not significantly improve calibration accuracy.

Conclusions:

The APACHE II and its customised version should not be used in the local setting as they both have poor calibration. There is an urgent need for larger studies to perform second-level customisation or to develop a new prognostic model tailored to the Singapore critical care setting.

The Ability of the Acute Physiology and Chronic Health Evaluation (APACHE) IV Score to Predict Mortality in a Single Tertiary Hospital

Background

The Acute Physiology and Chronic Health Evaluation (APACHE) II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs). The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion.

Methods

The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR).

Results

The APACHE IV score, the Charlson Comorbidity index (CCI) score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00). However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70).

Conclusions

The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

Validation of the Pediatric Index of Mortality 3 in a Single Pediatric Intensive Care Unit in Korea

To compare mortality rate, the adjustment of case-mix variables is needed. The Pediatric Index of Mortality (PIM) 3 score is a widely used case-mix adjustment system of a pediatric intensive care unit (ICU), but there has been no validation study of it in Korea. We aim to validate the PIM3 in a Korean pediatric ICU, and extend the validation of the score from those aged 0-16 to 0-18 years, as patients aged 16-18 years are admitted to pediatric ICU in Korea. A retrospective cohort study of 1,710 patients was conducted in a tertiary pediatric ICU. To validate the score, the discriminatory power was assessed by calculating the area under the receiver-operating characteristic (ROC) curve, and calibration was evaluated by the Hosmer-Lemeshow goodness-of-fit (GOF) test. The observed mortality rate was 8.47%, and the predicted mortality rate was 6.57%. For patients aged < 18 years, the discrimination was acceptable (c-index = 0.76) and the calibration was good, with a χ² of 9.4 in the GOF test (P = 0.313). The observed mortality rate in the hemato-oncological subgroup was high (18.73%), as compared to the predicted mortality rate (7.13%), and the discrimination was unacceptable (c-index = 0.66). In conclusion, the PIM3 performed well in a Korean pediatric ICU. However, the application of the PIM3 to a hemato-oncological subgroup needs to be cautioned. Further studies on the performance of PIM3 in pediatric patients in adult ICUs and pediatric ICUs of primary and secondary hospitals are needed.

Role of acute physiology and chronic health evaluation II scoring system in determining the severity and prognosis of critically ill patients in pediatric intensive care unit

Objective:

This study was conducted to validate the use of Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring system in pediatric population in predicting the risk of mortality and to compare the predicted death rate (using APACHE II) with the actual death rate of the patients.

Design:

Hospital-based prospective study.

Setting:

Tertiary care Pediatric Intensive Care Unit (PICU) in Western Rajasthan, India.

Methods:

A total of 100 critically ill children between 1 and 18 years of age admitted to PICU and fulfilling the inclusion criteria were enrolled. APACHE II score was calculated in each patient on the day of admission. The predicted mortality was calculated on the basis of this score.

Results:

The mean APACHE II score was 21.35 ± 5.76. Mean APACHE II score among the survivors was 16.60 ± 6.12, and mean APACHE II score among the nonsurvivors was 26.11 ± 5.41, and the difference was statistically significant (P = 0.00). The area under the receiver operating characteristic curve for APACHE II score was found to be 0.889 (P = 0.008) indicating good discrimination.

Conclusion:

APACHE II scoring system has a good discrimination and calibration when applied to a pediatric population.