Cohort study of the APACHE II score and mortality for different types of intensive care unit patients.

OBJECTIVES: Find the discriminant and calibration of APACHE II (Acute Physiology And Chronic Health Evaluation) score to predict mortality for different type of intensive care unit (ICU) patients. METHODS: This is a cohort retrospective study using secondary data of ICU patients admitted to Siloam Hospital of Lippo Village from 2014 to 2018 with minimum age ≥17 years. The analysis uses the receiver operating characteristic curve, student t-test and logistic regression to find significant variables needed to predict mortality. RESULTS: A total of 2181 ICU patients: men (55.52%) and women (44.48%) with an average age of 53.8 years old and length of stay 3.92 days were included in this study. Patients were admitted from medical emergency (30.5%), neurosurgical (52.1%) and surgical (17.4%) departments, with 10% of mortality proportion. Patients admitted from the medical emergency had the highest average APACHE score, 23.14±8.5, compared with patients admitted from neurosurgery 15.3±6.6 and surgical 15.8±6.8. The mortality rate of patients from medical emergency (24.5%) was higher than patients from neurosurgery (3.5%) or surgical (5.3%) departments. Area under curve of APACHE II score showed 0.8536 (95% CI 0.827 to 0.879). The goodness of fit Hosmer-Lemeshow show p=0.000 with all ICU patients' mortality; p=0.641 with medical emergency, p=0.0001 with neurosurgical and p=0.000 with surgical patients. CONCLUSION: APACHE II has a good discriminant for predicting mortality among ICU patients in Siloam Hospital but poor calibration score. However, it demonstrates poor calibration in neurosurgical and surgical patients while demonstrating adequate calibration in medical emergency patients.

Prevalence of tuberculosis infection and its relationship to stunting in children (under five years) household contact with new tuberculosis cases.

BACKGROUND: Children who inhabit the same house with tuberculosis (TB) patients are at high risk for infection and illness with TB. Nutritional status (stunting) in children is related to the child's ability to withstand MTB (Mycobacterium Tuberculosis). This study aims to estimated the prevalence of tuberculosis infection and its relationship to stunting in children (under five years) with household contact (HHC) with new TB cases. METHODS: A cross-sectional design was implemented. Conducted in July 2018-April 2019 at 13 Public Health Center in Makassar City. The sample size was calculated using one sample situation-about precision formula. Samples were children under five who had contact with new diagnosed TB cases. Tuberculosis infection was measured by TST (tuberculin skin test). Logistic regression with causal model to examine TB infection relationship with stunting and covariate variable, analyzed using Stata/MP 13.0 software. RESULTS: One hundred twenty-six (126) eligible children. Prevalence of tuberculosis infection was 38.10%. Frequency of stunted was 31 children (24.60%). Stunted nutritional status (aPR): 2.36, 95% CI 1.60-3.44), boys (aPR: 1.47, 95% CI 0.96-2.25), not getting BCG immunization (aPR: 1.58, 95%) CI 0.89-2.82), and high contact intensity (aPR: 2.62, 95% CI 1.10-6.22) best predicted the tuberculosis infection in children with TB case household contacts with a model contribution of 64%. CONCLUSION: Stunted nutritional status (moderate and severe), boys, not getting BCG immunization, and high contact intensity are the determinants of TB infection transmission in children HHC with TB. Children under five years of age who have close contact with TB cases should be targeted for priority interventions to prevent the transmission of TB infection and progressing to TB cases.

Decision Tree Clinical Algorithm for Screening of Mild Cognitive Impairment in the Elderly in Primary Health Care: Development, Test of Accuracy, and Time-Effectiveness Analysis.

Mild cognitive impairment (MCI) is predicted to be a common cognitive impairment in primary health care. Early detection and appropriate management of MCI can slow the rate of deterioration in cognitive deficits. The current methods for early detection of MCI have not been satisfactory for some doctors in primary health care. Therefore, an easy, fast, accurate and reliable method for screening of MCI in primary health care is needed. This study intends to develop a decision tree clinical algorithm based on a combination of simple neurological physical examination and brief cognitive assessment for distinguishing elderly with MCI from normal elderly in primary health care. This is a diagnostic study, comparative analysis in elderly with normal cognition and those presenting with MCI. We enrolled 212 elderly people aged 60.04-79.92 years old. Multivariate statistical analysis showed that the existence of subjective memory complaints, history of lack of physical exercise, abnormal verbal semantic fluency, and poor one-leg balance were found to be predictors of MCI diagnosis (p ≤ 0.001; p = 0.036; p ≤ 0.001; p = 0.013). The decision trees clinical algorithm, which is a combination of these variables, has a fairly good accuracy in distinguishing elderly with MCI from normal elderly (accuracy = 89.62%; sensitivity = 71.05%; specificity = 100%; positive predictive value = 100%; negative predictive value = 86.08%; negative likelihood ratio = 0.29; and time effectiveness ratio = 3.03). These results suggest that the decision tree clinical algorithm can be used for screening of MCI in the elderly in primary health care.