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Gao L, Bian Y, Cao S, Sang W, Zhang Q, Yuan Q, Xu F, Chen Y. Development and Validation of a Simple-to-Use Nomogram for Predicting In-Hospital Mortality in Patients With Acute Heart Failure Undergoing Continuous Renal Replacement Therapy. Front Med (Lausanne) 2021; 8:678252. [PMID: 34805193 PMCID: PMC8595094 DOI: 10.3389/fmed.2021.678252] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/27/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Patients with acute heart failure (AHF) who require continuous renal replacement therapy (CRRT) have a high risk of in-hospital mortality. It is clinically important to screen high-risk patients using a model or scoring system. This study aimed to develop and validate a simple-to-use nomogram consisting of independent prognostic variables for the prediction of in-hospital mortality in patients with AHF undergoing CRRT. Methods: We collected clinical data for 121 patients with a diagnosis of AHF who underwent CRRT in an AHF unit between September 2011 and August 2020 and from 105 patients in the medical information mart for intensive care III (MIMIC-III) database. The nomogram model was created using a visual processing logistic regression model and verified using the standard method. Results: Patient age, days after admission, lactic acid level, blood glucose concentration, and diastolic blood pressure were the significant prognostic factors in the logistic regression analyses and were included in our model (named D-GLAD) as predictors. The resulting model containing the above-mentioned five factors had good discrimination ability in both the training group (C-index, 0.829) and the validation group (C-index, 0.740). The calibration and clinical effectiveness showed the nomogram to be accurate for the prediction of in-hospital mortality in both the training and validation cohort when compared with other models. The in-hospital mortality rates in the low-risk, moderate-risk, and high-risk groups were 14.46, 40.74, and 71.91%, respectively. Conclusion: The nomogram allowed the optimal prediction of in-hospital mortality in adults with AHF undergoing CRRT. Using this simple-to-use model, the in-hospital mortality risk can be determined for an individual patient and could be useful for the early identification of high-risk patients. An online version of the D-GLAD model can be accessed at https://ahfcrrt-d-glad.shinyapps.io/DynNomapp/. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT0751838.
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Affiliation(s)
- Luyao Gao
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Yuan Bian
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Shengchuan Cao
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Wentao Sang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Qun Zhang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Qiuhuan Yuan
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Xu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Yuguo Chen
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- The Key Laboratory of Cardiovascular Remodeling and Function Research, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
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Kim H, Kim K. [Verification of validity of MPM II for neurological patients in intensive care units]. J Korean Acad Nurs 2011; 41:92-100. [PMID: 21516003 DOI: 10.4040/jkan.2011.41.1.92] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
PURPOSE Mortality Probability Model (MPM) II is a model for predicting mortality probability of patients admitted to ICU. This study was done to test the validity of MPM II for critically ill neurological patients and to determine applicability of MPM II in predicting mortality of neurological ICU patients. METHODS Data were collected from medical records of 187 neurological patients over 18 yr of age who were admitted to the ICU of C University Hospital during the period from January 2008 to May 2009. Collected data were analyzed through χ(2) test, t-test, Mann-Whiteny test, goodness of fit test, and ROC curve. RESULTS As to mortality according to patients' general and clinically related characteristics, mortality was statistically significantly different for ICU stay, hospital stay, APACHE III score, APACHE predicted death rate, GCS, endotracheal intubation, and central venous catheter. Results of Hosmer-Lemeshow goodness-of-fit test were MPM II(0) (χ(2)=0.02, p=.989), MPM II(24) (χ(2)=0.99 p=.805), MPM II(48) (χ(2)=0.91, p=.822), and MPM II(72) (χ(2)=1.57, p=.457), and results of the discrimination test using the ROC curve were MPM II(0), .726 (p<.001), MPM II(24), .764 (p<.001), MPM II(48), .762 (p<.001), and MPM II(72), .809 (p<.001). CONCLUSION MPM II was found to be a valid mortality prediction model for neurological ICU patients.
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Affiliation(s)
- Heejeong Kim
- Department of Nursing, Namseoul University, Cheonan, Korea
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Chatzicostas C, Roussomoustakaki M, Notas G, Vlachonikolis IG, Samonakis D, Romanos J, Vardas E, Kouroumalis EA. A comparison of Child-Pugh, APACHE II and APACHE III scoring systems in predicting hospital mortality of patients with liver cirrhosis. BMC Gastroenterol 2003; 3:7. [PMID: 12735793 PMCID: PMC156886 DOI: 10.1186/1471-230x-3-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2002] [Accepted: 05/08/2003] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The aim of this study was to assess the prognostic accuracy of Child-Pugh and APACHE II and III scoring systems in predicting short-term, hospital mortality of patients with liver cirrhosis. METHODS 200 admissions of 147 cirrhotic patients (44% viral-associated liver cirrhosis, 33% alcoholic, 18.5% cryptogenic, 4.5% both viral and alcoholic) were studied prospectively. Clinical and laboratory data conforming to the Child-Pugh, APACHE II and III scores were recorded on day 1 for all patients. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under a ROC curve (AUC). Calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test. RESULTS Overall mortality was 11.5%. The mean Child-Pugh, APACHE II and III scores for survivors were found to be significantly lower than those of nonsurvivors. Discrimination was excellent for Child-Pugh (ROC AUC: 0.859) and APACHE III (ROC AUC: 0.816) scores, and acceptable for APACHE II score (ROC AUC: 0.759). Although the Hosmer-Lemeshow statistic revealed adequate goodness-of-fit for Child-Pugh score (P = 0.192), this was not the case for APACHE II and III scores (P = 0.004 and 0.003 respectively) CONCLUSION Our results indicate that, of the three models, Child-Pugh score had the least statistically significant discrepancy between predicted and observed mortality across the strata of increasing predicting mortality. This supports the hypothesis that APACHE scores do not work accurately outside ICU settings.
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Affiliation(s)
| | | | - Georgios Notas
- Liver Research Laboratory, University of Crete Medical School, Greece
| | | | - Demetrios Samonakis
- Department of Gastroenterology, University Hospital, Heraklion, Crete, Greece
| | - John Romanos
- Department of Surgical Oncology, University Hospital, Heraklion, Crete, Greece
| | - Emmanouel Vardas
- Department of Gastroenterology, University Hospital, Heraklion, Crete, Greece
| | - Elias A Kouroumalis
- Department of Gastroenterology, University Hospital, Heraklion, Crete, Greece
- Liver Research Laboratory, University of Crete Medical School, Greece
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Arabi Y, Haddad S, Goraj R, Al-Shimemeri A, Al-Malik S. Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit. Crit Care 2002; 6:166-74. [PMID: 11983044 PMCID: PMC111184 DOI: 10.1186/cc1477] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2001] [Revised: 01/24/2002] [Accepted: 02/05/2002] [Indexed: 02/24/2023] Open
Abstract
INTRODUCTION The purpose of this study is to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality Probability Model MPM II0 and MPM II24 systems in a major tertiary care hospital in Riyadh, Saudi Arabia. METHODS The following data were collected prospectively on all consecutive patients admitted to the Intensive Care Unit between 1 March 1999 and 31 December 2000: demographics, APACHE II and SAPS II scores, MPM variables, ICU and hospital outcome. Predicted mortality was calculated using original regression formulas. Standardized mortality ratio (SMR) was computed with 95% confidence intervals (CI). Calibration was assessed by calculating Lemeshow-Hosmer goodness-of-fit C statistics. Discrimination was evaluated by calculating the Area Under the Receiver Operating Characteristic Curves (ROC AUC). RESULTS Predicted mortality by all systems was not significantly different from actual mortality [SMR for MPM II0: 1.00 (0.91-1.10), APACHE II: 1.00 (0.8-1.11), SAPS II: 1.09 (0.97-1.21), MPM II24 0.92 (0.82-1.03)]. Calibration was best for MPM II24 (C-statistic: 14.71, P = 0.06). Discrimination was best for MPM II0 (ROC AUC:0.85) followed by MPM II24 (0.84), APACHE II (0.83) then SAPS II (0.79). CONCLUSIONS In our ICU population: 1) Overall mortality prediction, estimated by standardized mortality ratio, was accurate, especially for MPM II0 and APACHE II. 2) MPM II24 has the best calibration. 3) SAPS II has the lowest calibration and discrimination. The local performance of MPM II24 in addition to its ease-to-use makes it an attractive model for mortality prediction in Saudi Arabia.
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Affiliation(s)
- Yaseen Arabi
- Consultant ICU Program Director, Critical Care Fellowship, King Fahad National Guard Hospital, Riyadh, Saudi Arabia.
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Rué M, Roqué M, Solà J, Macià M. [Probabilistic models of mortality for patients hospitalized in conventional units]. Med Clin (Barc) 2001; 117:326-31. [PMID: 11749903 DOI: 10.1016/s0025-7753(01)72103-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND We have developed a tool to measure disease severity of patients hospitalized in conventional units in order to evaluate and compare the effectiveness and quality of health care in our setting. PATIENTS AND METHOD A total of 2,274 adult patients admitted consecutively to inpatient units from the Medicine, Surgery and Orthopaedic Surgery, and Trauma Departments of the Corporació Sanitària Parc Taulí of Sabadell, Spain, between November 1, 1997 and September 30, 1998 were included. The following variables were collected: demographic data, previous health state, substance abuse, comorbidity prior to admission, characteristics of the admission, clinical parameters within the first 24 hours of admission, laboratory results and data from the Basic Minimum Data Set of hospital discharges. Multiple logistic regression analysis was used to develop mortality probability models during the hospital stay. RESULTS The mortality probability model at admission (MPMHOS-0) contained 7 variables associated with mortality during hospital stay: age, urgent admission, chronic cardiac insufficiency, chronic respiratory insufficiency, chronic liver disease, neoplasm, and dementia syndrome. The mortality probability model at 24-48 hours from admission (MPMHOS-24) contained 9 variables: those included in the MPMHOS-0 plus two statistically significant laboratory variables: hemoglobin and creatinine. CONCLUSIONS Severity measures, in particular those presented in this study, can be helpful for the interpretation of hospital mortality rates and can guide mortality or quality committees at the time of investigating health care-related problems.
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Affiliation(s)
- M Rué
- Corporació Sanitària Parc Taulí, Sabadell, Barcelona.
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