Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients

The modified Glasgow prognostic score (MGPS) and the mortality prediction model II (MPM II) can predict mortality in patients with breast cancer admitted to intensive care: A retrospective cohort study

Background/Aim: Breast cancer is the second most common cause of cancer-related death in women worldwide. Predicting the prognosis in breast cancer with very high mortality is important in terms of disease treatment and increasing life expectancy. In our study, we aimed to examine the importance of some inflammatory markers and scoring systems in predicting prognosis in patients with breast cancer who were hospitalized in the intensive care unit. Methods: This retrospective cohort study was conducted in the Department of Medical Oncology and Intensive Care Unit between 2014 and 2020. Breast cancer patients who were admitted to the intensive care unit at any stage of their treatment during the study and followed up and treated in the medical oncology department of the hospital were included in the study. All data were compared between groups (discharged or exitus) based on survival status. Socio-demographic information, laboratory findings (hemoglobin, leukocytes, neutrophils, lymphocytes, platelets, eosinophils, monocytes, C-reactive protein [CRP], albumin, lactate dehydrogenase [LDH], clinical status [co-morbidities, length of stay in intensive care, mechanical ventilation, and reason for hospitalization in the intensive care unit]), and survival data of the patients were collected retrospectively from hospital medical records. We also recorded treatment-related data and relapse/progression information. Neutrophil–lymphocyte, platelet–lymphocyte, and lymphocyte–monocyte ratios (NLR, PLR, and LMR, respectively) were calculated. Results: Thirty-seven (52.1%) patients died and 34 (47.9%) patients survived. The NLR (P=0.021), Modified Glasgow Prognostic score (P<0.001), APACHE II score (P<0.001) and mortality probability model (MPM II) upon admission (P<0.001) were significantly higher in the exitus group than in the survivors. The lymphocyte_monocyte ratio (P=0.030) and prognostic nutritional index (P=0.004) were significantly higher in the discharged group than in the death group. When we evaluated performance of the prognostic scores to predict mortality, we found that the APACHE II score (area under the curve [AUC]: 0.939, 95% confidence interval [CI]: 0.888–0.990), MPM II-Admission (AUC: 0.936, 95% CI: 0.880–0.992), and modified Glasgow Prognostic Score ([mGPS] AUC: 0.727, 95% CI: 0.600–0.854) had the highest area under curve values. Multivariable regression revealed that longer chemotherapy duration (≥2 weeks), an mGPS score of two points, and high MPM-II (≥36 points) were independently associated with mortality. Conclusion: Among the inflammatory markers and scores examined, mGPS and MPM-II were found to be independently associated with mortality in breast cancer patients who were hospitalized in the intensive care unit. In addition, patients with longer chemotherapy duration had a higher risk of mortality, but this result was limited by various possible confounders.

Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients

APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient’s ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient’s ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient’s treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.

Epidemiology, Clinical Characteristics, and Prognostic Factors in Critically Ill Patients with Hematolymphoid Malignancy

Objective

Despite advances in the field of oncology and intensive care, the outcomes of hematolymphoid malignancy (HLM) patients admitted to ICU are poor. This study was carried out to look at the demographic data, clinical features, and predictors of hospital mortality in these patients.

Materials and methods

We prospectively studied 101 adult critically ill patients with HLM admitted to the 14-bedded mixed medical surgical ICU of a tertiary care cancer center. Out of 101 patients, end-of-life care decisions were taken in 7 patients, who were excluded from the outcome analysis. Predictors of in-hospital mortality were evaluated using univariate and multivariate analysis.

Results

The ICU and in-hospital mortality recorded in our study were 48.9 and 54.3%, respectively. Neutropenia at ICU admission, Simplified Acute Physiology Score III (SAPS III) score, and mechanical ventilation (MV) within 24 hours of ICU admission were associated with in-hospital mortality on univariate analysis. On multivariate logistic regression analysis, neutropenia at ICU admission (OR 4.621; 95% CI, 1.2–17.357) and MV within 24 hours of ICU admission (OR 2.728; 95% CI, 1.077–6.912) were independent predictors of in-hospital mortality.

Conclusion

The HLM patients needing critical care have high acuity of illness, and acute respiratory failure is the commonest reason for ICU admission in these patients. In our study, the ICU survival was more than 50% and more than 45% patients were discharged alive from the hospital. We found a need for MV within 24 hours of ICU admission and presence of neutropenia at ICU admission to be independent predictors of hospital mortality in our study.

How to cite this article

Siddiqui SS, Prabu NR, Chaudhari HK, Narkhede AM, Sarode SV, Dhundi U, et al. Epidemiology, Clinical Characteristics, and Prognostic Factors in Critically Ill Patients with Hematolymphoid Malignancy. Indian J Crit Care Med 2021;25(1):56–61.

Ideal Severity of Illness Scoring System for Critically Ill Cancer Patients: A Dream

How to cite this article: Krishna B. Ideal Severity of Illness Scoring System for Critically Ill Cancer Patients: A Dream. Indian J Crit Care Med 2020;24(4):215.