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Accuracy of Pediatric Risk of Mortality (PRISM) III Score in Predicting Mortality Outcomes in a Pediatric Intensive
Sadiq Mirza1, Laraib Malik2, Jawad Ahmed3
1Pediatric Critical Care, The Indus Hospital, Karachi, PAK.
Insights
The Pediatric Risk of Mortality III (PRISM III) score accurately predicts mortality in pediatric intensive care units. This score helps identify critically ill children needing urgent care, especially in resource-limited settings.
Area of Science:
- Pediatric Intensive Care
- Clinical Epidemiology
- Biostatistics
Background:
- Pediatric intensive care units (PICUs) are crucial for managing critically ill children.
- Accurate mortality prediction scales are vital for resource allocation and patient management.
- Pediatric Risk of Mortality (PRISM) and Pediatric Index of Mortality (PIM) are commonly used predictive tools.
Purpose of the Study:
- To evaluate the predictive accuracy of the Pediatric Risk of Mortality III (PRISM III) score in a tertiary care hospital's PICU.
- To assess the correlation between PRISM III scores and patient mortality.
- To determine the utility of PRISM III in resource-limited settings.
Main Methods:
- A prospective hospital-based study conducted in a PICU from December 2017 to June 2019.
- Inclusion of 407 patients aged one month to 12 years.
- Calculation of PRISM III scores at 24 hours of admission using a standard questionnaire.
Main Results:
- The overall mortality rate was 37.35% (152 out of 407 patients).
- Factors associated with poor survival included mechanical ventilation, inotropic drug use, higher temperatures, and low Glasgow Coma Scale scores.
- PRISM III demonstrated good predictive power with an Area Under the Curve of 0.903±0.016 (p<0.001).
Conclusions:
- The PRISM III score exhibits excellent accuracy and predictive ability in the studied pediatric population.
- Observed and expected mortality rates showed no significant difference, validating the score's performance.
- PRISM III aids in identifying high-risk patients and assessing prognosis, crucial for resource allocation in underserved areas.
Abstract:
Background With the advancements in medicine and increasing access to modern technology, pediatric intensive care units (PICU) are becoming a vital part of any health care setting. PICUs play a key role in saving the life of young patients. Various scales have been designed by researchers to aid in predicting the mortality of a patient admitted in PICU. Pediatric Risk of Mortality (PRISM) and Pediatric Index of Mortality (PIM) are among the most commonly used scales. Calculating the risk of mortality enables the physicians to classify the patients and helps in identifying which patients require more urgent care and resources. Methods A hospital-based prospective study was carried out at PICU in a tertiary care hospital in Karachi from December 2017 to June 2019. All patients between the age of one month and 12 years were included in our study after informed consent from parents/guardians. A standard questionnaire was used and the PRISM III score was calculated at 24 hours of admission. All necessary investigations were carried out, and all statistical analyses were carried out using SPSS v.23 (IBM, Armonk, NY). Results A total of 407 patients were included in our study with the majority being males (54.5%). The mean age was 27±33 months. The mean duration of stay of patients in PICU was 80.15±36.58 hours. The mortality rate in our study was 37.35 % (n=152). The need for mechanical ventilation, use of inotropic drugs, higher temperatures, and low Glasgow Coma Scale scores were associated with poor survival. It was noted that as the PRISM III score increased, the mortality rate also increased. In our study, we found that PRISM III had good predictive power in our population. The area under the curve was 0.903±0.016 (p<0.001, 95% confidence interval: 0.872-0.934). Conclusions PRISM III score showed excellent accuracy and predictive ability in our population. There was no significant difference in observed and expected mortality rates in our study. In a resource-limited setting, the prediction models highlight the cases where more medical attention is required and also enable the physicians to assess the prognosis of the patient so adequate measures can be taken beforehand.
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