Apache II scoring in predicting surgical outcome in patients of perforation peritonitis

Agarwal, Anand; Choudhary, Garima; Bairwa, Mansingh; Choudhary, Amit Kumar

doi:10.18203/2349-2902.isj20172790

Cited by 11 publications

(14 citation statements)

References 7 publications

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“…12 Most patients were having low Apache II score; 42.6% having Apache II score 0 to 10 similar to scores seen by Agarwal A. 2 Local complications were encountered more in patients with low Apache II score whereas systemic complications were more common in patients with higher Apache II score. Apache II score correlated well with the outcome; with poor outcome (higher mortality rate) seen with higher score.…”

Section: Discussionsupporting

confidence: 61%

“…1 In contrast to the Western literature, where lower gastrointestinal tract perforations predominate, upper gastrointestinal tract perforations constitute the majority of cases in India and the subcontinent. 2,3 Secondary peritonitis usually presents as acute generalized peritonitis which is a potentially life-Department of Surgery, Government Medical College Srinagar, Jammu and Kashmir, India threatening condition. It is a common surgical emergency in most of the general surgical units, across the world.…”

Section: Introductionmentioning

confidence: 99%

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Apache II score in predicting surgical outcome in patients of secondary peritonitis: a prospective and observational study

et al. 2020

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Background: Peritonitis is defined as inflammation of the serosal membrane that lines the abdominal cavity and the organs contained therein. Secondary peritonitis presenting as acute generalized peritonitis is a common surgical emergency often associated with significant morbidity and mortality. Many scoring systems have been found useful in predicting the outcome in critically ill patients, thus allowing application of resources for effective use. Amongst them acute physiology and chronic health evaluation score (APACHE II), have a strong relationship to the outcome than previous groupings without consideration for systemic effect of the intra-abdominal sepsis. Methods: This study was conducted in the Department of General surgery, Sri Maharaja Hari Singh (SMHS) Hospital an associated hospital with the Government Medical College Srinagar, J&K, India. The prospective study was conducted over a period from October 2016 to September 2018 (Two Year) on 108 patients diagnosed with secondary peritonitis. Data was collected and analysed using SPSS v 20. Results: study included 108 patients with males involving 74.1% (80). The mean age of our study was 34 yr. (2-88 yr.), and 21-40 yr. (44.5%) group was mostly involved. Pain abdomen was present in 100% patients followed by nausea/vomiting (88%). Higher the APACHE VII score higher were post-operative complications (31+ score group 100%), mortality (31+ score group 100%) and less hospital stay (31+ score group 1.5 days) due to increased mortality. Conclusions: APACHE II score correlated well with postoperative complications, outcome, hospital stay. However, in patients with very high Apache score more than 30, the mean duration of hospital stay is less due to associated increased mortality during early Hospital stay.

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Section: Discussionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Apache II score in predicting surgical outcome in patients of secondary peritonitis: a prospective and observational study

et al. 2020

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“…Using regression analysis in the present study, the APACHE II scoring was shown to be an independent risk factor for SSI (p=0.04). Agarwal et al (2018) 20 also reported that Apache II scoring correlated well in predicting surgical outcome in patients of perforation peritonitis with the hospital and ICU stay. In a study conducted by Ishihata et al (2018) 21 the APACHE II score (13.0 ± 2.58) was significantly higher in patients with than in those without postoperative complications (P < 0.05).…”

Section: Discussionmentioning

confidence: 96%

<p>Care Bundle Approach to Reduce Surgical Site Infections in Acute Surgical Intensive Care Unit, Cairo, Egypt</p>

Wassef¹,

Mukhtar²,

Nabil³

et al. 2020

IDR

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Introduction: Surgical site infections (SSIs) are one of the most frequently reported hospital acquired infections associated with significant spread of antibiotic resistance. Purpose: We aimed to evaluate a bundle-based approach in reducing SSI at acute surgical intensive care unit of the Emergency Hospital of Cairo University. Patients and Methods: Our prospective study ran from March 2018 to February 2019 and used risk assessment. The study was divided into three phases. Phase I: (pre-bundle phase) for 5 months; data collection, active surveillance of the SSIs, screening for OXA-48 producing Enterobacteriaceae and multidrug resistant Acinetobacter baumannii colonizers using Chrom agars were carried out. Phase II: (bundle-implementation) a 6-S bundle approach included education, training and postoperative bathing with Chlorhexidine Gluconate in collaboration with the infection control team. Finally, Phase III: (post-implementation) for estimation of compliance, rates of colonization, and infection. Results: Phase I encompassed 177 patients, while Phase III included 93 patients. A significant reduction of colonization from 24% to 15% (p<0.001) was observed. Similarly, a decrease of SSI from 27% to 15% (p=0.02) was noticed. A logistic regression was performed to adjust for confounding in the implementation of the bundle and we found a 70% reduction of SSI odd's ratio (OR's ratio = 0.3) confidence interval (95% CI 0.14-0.6) with significant Apache II (p=0.04), type of wound; type II (p=0.002), type III (p=0.001) and duration of surgery (p=0.04) as independent risk factors for SSI. Klebsiella pneumoniae was the most prevalent organism during phase I (34.7%). On the other hand, A. baumannii was the commonest organism to be isolated during phase III with (38.5%) preceding K. pneumoniae (30%). Conclusion: Our study demonstrated that the implementation of a multidisciplinary bundle containing evidence-based interventions was associated with a significant reduction of colonization and SSIs and was met with staff approval and acceptable compliance.

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“…For instance, the SOFA score has previously been shown to have a 473 significant prognostic value for in-hospital mortality prediction [46] [47]. The APACHE 474 II score is often used for outcome prediction too [48][49][50]. And in the study by Jansen et 475 al.…”

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confidence: 99%

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

Aushev

Ripoll

Vellido

et al. 2018

Preprint

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Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.1 Shock (or circulatory shock) is a life-threatening medical condition that requires 2 immediate treatment. It is prevalent in the Intensive Care Unit (ICU) and a major 3 concern in Critical Care in general. This condition occurs when the organs and tissues 4 of the body do not receive enough blood and, as a result, cells see their oxygen and 5 nutrients supply restricted so that organs become damaged. Hypotension, tissue 6 hypoperfusion and hyperlactatemia are amongst the most common symptoms [28]. The 7 outcome of an individual case depends on the stage of shock, the underlying condition 8 and the general medical state of the patient [28]. 9Four types of shock are commonly defined: hypovolaemic shock (e.g. hemorrhagic 10 shock), cardiogenic shock, distributive shock (e.g. septic shock) and obstructive shock. 11 The mortality rate of the condition remains very high and depends on its type. It is 12 quantified at 30% for septic shock (according to the new definition of such condition [29]) 13 and between 35.9% and 64.7% for in-hospital mortality in patients with cardiogenic 14 shock (depending on the stage, [30]). Usually, shock is treated in the ICU, where it is 15 carefully monitored. As a result, large amounts of data are produced for each patient. 16 Machine Learning (ML) techniques can help find a list of attributes to predict the 17 outcome of shock patients from clinical data (i.e. data routinely monitored in the ICU). 18 However, there is a large amount of attributes that characterize each patient, ranging 19 from base pathology information, to lab procedures or hemodynamic data and 20 treatment, to name a few. As a result, data are likely to be very high di...

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Apache II scoring in predicting surgical outcome in patients of perforation peritonitis

Cited by 11 publications

References 7 publications

Apache II score in predicting surgical outcome in patients of secondary peritonitis: a prospective and observational study

Apache II score in predicting surgical outcome in patients of secondary peritonitis: a prospective and observational study

<p>Care Bundle Approach to Reduce Surgical Site Infections in Acute Surgical Intensive Care Unit, Cairo, Egypt</p>

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

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