The Junior et al. 2015, Exarchopoulos et al.

The observed 30-day mortality was 10.3 % which is
considered to be  higher than the average
mortality reported in previous studies 9.3%, 9.6% and
6% (Curiel-Balsera et al. 2013, Junior et al. 2015,
Exarchopoulos et al. 2015) respectively. this may be due to The higher rate
of postoperative cardiac and respiratory complications. twenty seven/103 (26.2%)
of the study group had cardiac complications while 16/103 patients (15.5%) had
respiratory complications.

A factor that was identified in this study as an
independent predictor of mortality after cardiac surgery, was the preoperative
platelet count. We found that the preoperative platelet count was higher in non
survivors {285.40+67.42 (103)} compared to survivors {232.32+64.41
(103)}. Unal et al. 2013 reported that the mean platelet
volume (MPV) reflecting platelet production rate and activation and the
platelet count were moderately correlated with adverse events after CABG
including ischemic vascular events, recurrent MI or death. The reported platelet count in their patients with adverse
events was 262 ± 66(103).

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score, calculated in the first day of ICU admission, was identified as another
independent predictor of postoperative mortality. It has a good predictive
power for the 30-day mortality after cardiac surgery (AUC: 0.868, p value
<0.001). Supporting our results Chang et al. 2017 studied 483 patients after CABG and found that APACHE II score in the first ICU day was effective in prediction of mortality (AUC: 0.86, P value <0.001).  Other authors demonstrated that APACHE II score at ICU admission successfully predicted 30-day mortality in 150 cardiac surgery patients (AUC: 0.82, P value 0.001) (Exarchopoulos et al. 2015). The most important difference between APACHE II score and other scores is that it is estimated during the first 24 hours of ICU admission so it gives a snapshot of risk using data in the early time of admission but it still cannot guide clinical decision making reliably after the initial ICU period and prediction could be inaccurate as postoperative events have not occurred yet (Howitt et al. 2016). This can be solved if APACHE II score have the ability to predict the risk daily. In our study the maximum SOFA score in the first four days well predicted the 30-day mortality after cardiac surgery (AUC: 0.878, P value 0.001). The daily SOFA score showed significant results in all days with the 3rd day being the best (AUC: 0.918, P value: 0.001) in prediction of mortality. this is in accordance with Patila et al. 2006 who calculated the SOFA for 857 cardiac surgery patients, the SOFA score in the first three days showed good discrimination for mortality with the overall maximum SOFA score being slightly better(AUC: 0.76) and Ceriani et al. 2003 who calculated SOFA score for the first 10 postoperative days in cardiac surgery patients, the worst score, the maximum score, the difference between the two values and the first day SOFA score.  All the four derivatives showed good discrimination with the worst daily score demonstrating the best performance. A word of caution about SOFA score is that the cardiovascular component of the SOFA score is based on the administration of vasoactive medication using specific protocols such as dopamine being administered before noradrenaline to treat hypotension. In many centers, clinicians know that these patterns of drug administration are not followed and this may lead to diminished confidence in the SOFA score despite reports of good performance in multiple studies ( Badreldin et al. 2012, Patila et al. 2006, Doerr et al. 2011, Exarchopoulos et al.2015). Regarding CASUS score, the statistical analysis showed that the maximum CASUS score was  not significant as a predictor  of  30-day mortality after cardiac surgery (AUC: 0.673, P value: 0.105). On the contrary to our results, the  mean and maximum CASUS score were validated for prediction of 30-day mortality by Doerr at al. 2011 and performed well in the first 6 postoperative days after cardiac surgery with maximum CASUS score (AUC: >90) and Exarchopoulos et al. 2015 who found that CASUS score showed good
discrimination and calibration in the first postoperative day after cardiac
surgery with AUC 0.89. The poor results of CASUS score in our study
in comparison to other studies  may be
attributed to  the difference in  patient populations as  it lacks its 
application  in different  countries 
and  it has not been tested in
multicenter studies and accordingly has not gained much popularity. Another reason is that CASUS score has some volatile
variables that may change from one hour to another such as lactate and pressure
adjusted heart rate (PAR).

Comparing the three scoring systems regarding their
predictive power of the 30-days mortality after cardiac surgery in this study,
showed that  APACHE II score and SOFA
score (AUC: 0.878) has a better ability to predict 30-day mortality than CASUS
score (AUC: 0.673).

Regarding prediction of morbidity, all
scores showed significant results in predicting length of ICU stay and
postoperative hours of ventilation, on the contrary none of the scores showed correlation
with the length of hospital stay as shown in table (5).

As shown in table (2) there is
statistically significant difference between survivors and non-survivors
regarding the value of SOFA (p: 0.001) and APACHE II (p: 0.001) scores.

ROC curves were plotted showing that both APACHE
II score(0.878) and SOFA score (0.878) have a good predictive power of 30-day
mortality after cardiac surgery compared with the poor predictive power of the CASUS
score (0.673) as shown in table (4).

However, multivariate analysis identified
APACHE II score and preoperative platelets count as the independent predictors
of mortality after cardiac surgery in as shown in table (3).

our study was conducted on 103 adult patients
with 30-day mortality 10.3%, all patients preoperative
characteristics are shown in table (1) showing that EF, platelets count, PO2
and bilirubin level are predictors of  mortality after cardiac surgery.

Mortality after cardiac surgery ranges from
2.94 to 32.5% depending on type of surgery and different populations (Mehta
et al. 2002, Serigar et al. 2013, Chang et al. 2016). numerous risk scores were
developed for mortality prediction after cardiac surgery but still there are some
major differences among these scores with regard to score design and the
initial population on which the score was developed (Geissler et al. 2000). Postoperative risk scoring gives
information of the postoperative situation, In addition to the preoperative
patient condition (Pätilä et al. 2006). Postoperative risk scoring systems such as the Cardiac
Surgery Score (CASUS), the Acute Physiology and Chronic Health Evaluation
(APACHE II) and the Sequential Organ Failure Assessment (SOFA) score are used to
predict mortality after cardiac surgery but they were not tested on our patient
population so we compared the accuracy of CASUS, APACHE II and SOFA scores in
predicting mortality after cardiac surgery in our centre.

Patient and methods:

Our prospective study comprised 103 adult
patients who underwent open heart surgery in the  Cardiothoracic Surgery Department – Tanta
University Hospitals from October  2015 to December 2017. Data on the
preoperative, intraoperative and postoperative status was recorded for each
patient. the postoperative patient data was recorded every hour and the worst
data was taken every 24 hours to calculate the postoperative scores. the APACHE
2 score was calculated once in the first postoperative 24 hours, SOFA score was
calculated every 24 hours for maximum of 4 postoperative days, CASUS score was
calculated in the 2nd and 4th postoperative days. Clinical outcome was defined
as postoperative morbidity and 30-day mortality. Morbidity was expressed by:
duration of ventilatory support, length of stay in the postoperative
ICU and ward. For patients readmitted to the ICU, we considered only the
initial stay in the ICU. In cases of re intubation, we considered only the
initial period of mechanical ventilation.

Statistical analysis:

The collected data were organized,
tabulated and statistically analyzed using SPSS version 19 (Statistical Package
for Social Studies) created by IBM, Illinois, Chicago, USA. For numerical
values the range mean and standard deviations were calculated. The differences
between two mean values were used using Mann-Whitney test as data were not
found to follow the normal distribution. For categorical variable the number
and percentage were calculated and differences between subcategories were
tested by Monte Carlo exact test. The correlation between two variables was
calculated using Pearson’s correlation coefficient. Linear regression was used
for multivariate analysis of numerical variables affecting survival. The ROC
curve was found to test predictability of survival by SOFA, CACUS and APACHE 2.
The level of significant was adopted at p<0.05.   


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