Photonic designs to reduce morbidity and mortality for ICU patients on ventilators

Prediction of Outcome in ICU Patients Using SAPS II Scoring-A Prospective Study

Indian Journal of Pharmaceutical and Biological Research ◽

10.30750/ijpbr.1.3.8 ◽

2013 ◽

Vol 1 (03) ◽

pp. 39-44 ◽

Cited By ~ 1

Author(s):

V. Harshini ◽

Chakrapani .

Keyword(s):

Prospective Study ◽

Scoring System ◽

Total Mortality ◽

Morbidity And Mortality ◽

P Value ◽

Icu Patients ◽

Saps Ii ◽

Icu Outcome ◽

Increased Risk ◽

Severity Scores

Introduction: Since 1981, several severity scores have been proposed for ICU patients. The first ones were acute physiology and chronic health evaluation(APACHE, APACHEII),Simplified acute physiology score (SAPS); later, mortality probability model(MPM) and APACHE III were introduced. The SAPS II scoring system, have been used as a method for converting the score to a probability of hospital mortality.The present prospective study is designed to predict the ICU outcome in medical ICU patients. Objective of the Study: To predict the mortality and morbidity of the patients admitted in ICU for various emergencies using SAPS II scoring system and correlate it with the outcome of the patient on discharge. Materials and Methods: The study prospective type, data was obtained from the patients admitted to ICU ,SAPS II scoring was given and were followed up till they got discharged to assess the outcome .Results:45 patients were studied the total mortality was 26.6%. The SAPS II Scores of the patients and the number of deaths in the different groups are given in table below. The chi-square value was 23.04, df= 6with a p + 0.0007 and this study is well within the p value of 0.05, hence it is significant which means the higher the score the more is the risk of morbidity and mortality, when score is >50 there is increased risk of morbidity and mortality, when score is >50 there is increased risk of mortality. Conclusion:The present study imposes on the following conclusions -SAPS II scoring is useful in predicting the ICU outcome of patients admitted in the ICU even when the primary diagnosis is not specified.

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Predictability of COVID-19-related morbidity and mortality based on model estimations to establish proactive protocols of countermeasures

Scientific Reports ◽

10.1038/s41598-021-93932-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Göran Svensson ◽

Rocio Rodriguez ◽

Carmen Padin

Keyword(s):

Predictive Ability ◽

Hospitalized Patients ◽

Morbidity And Mortality ◽

Unresolved Issue ◽

Icu Patients ◽

Healthcare Data ◽

The Future ◽

Virulent Virus

AbstractThe COVID-19 pandemic (SARS-CoV-2) has revealed the need for proactive protocols to react and act, imposing preventive and restrictive countermeasures on time in any society. The extent to which confirmed cases can predict the morbidity and mortality in a society remains an unresolved issue. The research objective is therefore to test a generic model’s predictability through time, based on percentage of confirmed cases on hospitalized patients, ICU patients and deceased. This study reports the explanatory and predictive ability of COVID-19-related healthcare data, such as whether there is a spread of a contagious and virulent virus in a society, and if so, whether the morbidity and mortality can be estimated in advance in the population. The model estimations stress the implementation of a pandemic strategy containing a proactive protocol entailing what, when, where, who and how countermeasures should be in place when a virulent virus (e.g. SARS-CoV-1, SARS-CoV-2 and MERS) or pandemic strikes next time. Several lessons for the future can be learnt from the reported model estimations. One lesson is that COVID-19-related morbidity and mortality in a population is indeed predictable. Another lesson is to have a proactive protocol of countermeasures in place.

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