APACHE II score and primary liver cancer history had risk of hospital mortality in patients with pyogenic liver abscess

Background: Aiming at diagnosing at an early stage, minimizing the misdiagnosis rate and improving the prognosis, the author has investigated the clinical characteristics, diagnosis and treatment of primary liver cancer mimicking liver abscess with a summary and discussion.Methods: All of the 11 cases of primary liver cancer mimicking liver abscess, diagnosed during January 2009 to December 2015, were retrospectively viewed in terms of clinical manifestations, laboratory tests, radiological feature, diagnosis and treatment. And statistic analysis was made in all aspects mentioned above with that of pyogenic liver abscess and other types of liver cancer diagnosed in the corresponding period. Results: The clinical manifestations of the 11 cases were mostly characterized by fever, abdominal pain and weight loss. There was no significantly statistic difference between the study group and any of the three matched groups in underlying disease and lab results. All of the 11 patients were treated with enhanced antibiotics as basic therapy. Furthermore, eight cases accepted surgical operation, among them, one object underwent puncture and drainage of the liver abscess by ultrasound (PDLA) twice pre-operation, one object underwent PDLA and hepatic arteriography pre-operation and death in hospital post-operation, one object suffered myocardial infarction post-operation. In addition, three cases received conservative treatment only, in which, one object died in hospital.Conclusions: It is difficult to distinguish primary liver cancer mimicking liver abscess from pyogenic liver abscess as there are no specific clinical manifestations and laboratory findings which is prone to leading to misdiagnosis. What’s worse, the prognosis is so poor that high alert and close follow-up are required as well as early diagnosis and treatment.

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Does pyogenic liver abscess increase the risk of delayed-onset primary liver cancer?

Medicine ◽

10.1097/md.0000000000007785 ◽

2017 ◽

Vol 96 (34) ◽

pp. e7785 ◽

Cited By ~ 12

Author(s):

Chia-Sheng Chu ◽

Che-Chen Lin ◽

Cheng-Yuan Peng ◽

Po-Heng Chuang ◽

Wen-Pang Su ◽

...

Keyword(s):

Liver Cancer ◽

Liver Abscess ◽

Primary Liver Cancer ◽

Pyogenic Liver Abscess ◽

Delayed Onset

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Aggressive hepatic resection for patients with pyogenic liver abscess and APACHE II score ≥15

The American Journal of Surgery ◽

10.1016/j.amjsurg.2007.09.051 ◽

2008 ◽

Vol 196 (3) ◽

pp. 346-350 ◽

Cited By ~ 26

Author(s):

Huan-Fa Hsieh ◽

Teng-Wei Chen ◽

Chih-Yung Yu ◽

Ning-Chi Wang ◽

Heng-Cheng Chu ◽

...

Keyword(s):

Hepatic Resection ◽

Liver Abscess ◽

Pyogenic Liver Abscess ◽

Apache Ii ◽

Apache Ii Score

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Utility of Acute Physiology and Chronic Health Evaluation (APACHE II) in Predicting Mortality in Patients with Pyogenic Liver Abscess: A Retrospective Study

Journal of Clinical Medicine ◽

10.3390/jcm10122644 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2644

Author(s):

Yuan-Ti Lee ◽

Chi-Chih Wang ◽

Chien-Feng Li ◽

Hsuan-Yi Chen ◽

Hsien-Hua Liao ◽

...

Keyword(s):

Multivariate Analysis ◽

Liver Abscess ◽

Pyogenic Liver Abscess ◽

Chronic Health Evaluation ◽

Apache Ii ◽

Apache Ii Score ◽

Health Evaluation ◽

Icu Patients ◽

Chronic Health ◽

Increased Risk

Pyogenic liver abscess (PLA) is a major life-threatening disease with varied clinical features. This study aimed to determine predictors of mortality in patients with PLA using criteria determined upon admission. We retrospectively examined the data of 324 hospitalized adults in whom liver abscesses were confirmed using abdominal ultrasound and/or computed tomography. The relationship between various risk factors was assessed using multivariate analysis. A total of 109 (33.6%) patients were admitted to the intensive care unit (ICU). The overall mortality rate was 7.4% and was higher among ICU patients than non-ICU patients (21.1% vs. 0.5%, p < 0.001). PLA patients with an Acute Physiology and Chronic Health Evaluation (APACHE) II score ≥18 had a 19.31-fold increased risk, and those with concomitant infections had a 34.33-fold increased risk of 30-day mortality according to multivariate analysis. The estimated area under the receiver operating characteristic curve for predicting 30-day mortality revealed that APACHE II score ≥18 (sensitivity of 75% and specificity of 84%, p < 0.0001) had better discriminative power than Sequential Organ Failure Assessment (SOFA) ≥6 (sensitivity of 81% and specificity of 66%, p < 0.0001). APACHE II has shown better discrimination ability than SOFA in predicting mortality in PLA patients. To improve outcomes in patients with PLA, future management strategies should focus on high-risk patients.

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The Relationship of the ACCP/SCCM Consensus Conference Classification of Sepsis to Mortality and Multiorgan Dysfunction among Medical ICU Patients

Journal of Intensive Care Medicine ◽

10.1177/088506669601100604 ◽

1996 ◽

Vol 11 (6) ◽

pp. 326-334 ◽

Cited By ~ 4

Author(s):

Marin H. Kollef ◽

Paul R. Eisenberg

Keyword(s):

Septic Shock ◽

Severe Sepsis ◽

Hospital Mortality ◽

Consensus Conference ◽

Organ System ◽

Apache Ii ◽

Apache Ii Score ◽

Icu Patients ◽

Multiorgan Dysfunction

To determine the relation between the proposed ACCP/SCCM Consensus Conference classification of sepsis and hospital outcomes, we conducted a single-center, prospective observational study at Barnes Hospital, St. Louis, MO, an academic tertiary care hospital. A total of 324 consecutive patients admitted to the medical intensive care unit (ICU) were studied for prospective patient surveillance and data collection. The main outcome measures were the number of acquired organ system derangements and hospital mortality. Fifty-seven (17.6%) patients died during the study period. The proposed classifications of sepsis (e.g., systemic inflammatory response syndrome [SIRS], sepsis, severe sepsis, septic shock) correlated with hospital mortality ( r = 0.330; p < 0.001) and development of an Organ System Failure Index (OSFI) of 3 or greater ( r = 0.426; p < 0.001). Independent determinants of hospital mortality for this patient cohort ( p < 0.05) were development of an OSFI of 3 or greater (adjusted odds ratio [AOR], 13.9; 95% confidence interval [CI], 6.4–30.2; p < 0.001); presence of severe sepsis or septic shock (AOR, 2.6; 95% CI, 1.2–5.6; p = 0.002), and an APACHE II score ≥ of 18 or greater (AOR, 2.4; 95% CI, 1.0–5.8; p = 0.045). Intra-abdominal infection (AOR, 19.1; 95% CI, 1.6–230.1; p = 0.011), an APACHE II score ≥ of 18 or greater (AOR, 8.9; 95% CI, 4.2–18.6; p < 0.001), and presence of severe sepsis or septic shock (AOR, 2.9; 95% CI, 1.5–5.4; p = 0.001) were independently associated with development of an OSFI of 3 or greater. These data confirm that acquired multiorgan dysfunction is the most important predictor of mortality among medical ICU patients. In addition, they identify the proposed ACCP/SCCM Consensus Conference classification of sepsis as an additional independent determinant of both hospital mortality and multiorgan dysfunction.

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Improvement of APACHE II score system for disease severity based on XGBoost algorithm

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01591-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yan Luo ◽

Zhiyu Wang ◽

Cong Wang

Keyword(s):

Intensive Care ◽

Hospital Mortality ◽

Patient Outcomes ◽

External Validation ◽

Apache Ii ◽

Validation Dataset ◽

Apache Ii Score ◽

Severity Scores ◽

The Impact ◽

Improve Patient

Abstract Background Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. Methods We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. Results We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. Conclusions As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

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Outcomes of Critically Ill Gynecological Cancer Patients Admitted to Intensive Care Unit

American Journal of Hospice and Palliative Medicine® ◽

10.1177/1049909112437028 ◽

2012 ◽

Vol 30 (1) ◽

pp. 7-11 ◽

Cited By ~ 4

Author(s):

Silvio A. Ñamendys-Silva ◽

María O. González-Herrera ◽

Julia Texcocano-Becerra ◽

Angel Herrera-Gómez

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Prognostic Factors ◽

Mortality Rate ◽

Hospital Mortality ◽

Critically Ill ◽

Critically Ill Patients ◽

Gynecological Cancer ◽

Apache Ii ◽

Apache Ii Score

Purpose: To assess the characteristics of critically ill patients with gynecological cancer, and to evaluate their prognosis. Methods: Fifty-two critically ill patients with gynecological cancer admitted to intensive care unit (ICU) were included. Univariate and multivariate logistic regressions were used to identify factors associated with hospital mortality. Results: Thirty-five patients (67.3%) had carcinoma of the cervix uteri and 11 (21.2%) had ovarian cancer. The mortality rate in the ICU was 17.3% (9 of 52) and hospital mortality rate were 23%(12 of 52). In the multivariate analysis, independent prognostic factors for hospital mortality were vasopressor use (odds ratio [OR] = 8.60, 95% confidence interval [CI] 2.05-36; P = .03) and the Acute Physiology and Chronic Health Evaluation (APACHE) II score (OR = 1.43, 95% CI 1.01-2.09; P = .048). Conclusions: The independent prognostic factors for hospital mortality were the need for vasopressors and the APACHE II score.

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