Association of Model for End-Stage Liver Disease Score With Mortality in Emergency General Surgery Patients | Critical Care Medicine | JAMA Surgery | JAMA Network
[Skip to Navigation]
Sign In
Figure.  Time-to-Event Curves for Mortality
Time-to-Event Curves for Mortality

Unadjusted mortality rates were calculated with Kaplan-Meier methods and compared with log-rank test. Categorization of risk groups is per the primary analysis. The global comparison log-rank P value is P < .001. MELD indicates Model for End-Stage Liver Disease.

Table 1.  Clinical and Demographic Characteristics of the 707 Included Patients
Clinical and Demographic Characteristics of the 707 Included Patients
Table 2.  Characteristics of the Study Cohort Stratified by MELD Score
Characteristics of the Study Cohort Stratified by MELD Score
Table 3.  Unadjusted and Adjusted Associations Between MELD Score and Mortality in 707 Patientsa
Unadjusted and Adjusted Associations Between MELD Score and Mortality in 707 Patientsa
1.
Centers for Disease Control and Prevention. Chronic liver disease and cirrhosis. http://www.cdc.gov/nchs/fastats/liver-disease.htm. Accessed December 21, 2015.
2.
Suman  A, Carey  WD.  Assessing the risk of surgery in patients with liver disease.  Cleve Clin J Med. 2006;73(4):398-404.PubMedGoogle ScholarCrossref
3.
Levesque  E, Hoti  E, Azoulay  D,  et al.  Prospective evaluation of the prognostic scores for cirrhotic patients admitted to an intensive care unit.  J Hepatol. 2012;56(1):95-102.PubMedGoogle ScholarCrossref
4.
Bosetti  C, Levi  F, Lucchini  F, Zatonski  WA, Negri  E, La Vecchia  C.  Worldwide mortality from cirrhosis: an update to 2002.  J Hepatol. 2007;46(5):827-839.PubMedGoogle ScholarCrossref
5.
Bittermann  T, Makar  G, Goldberg  DS.  Early post-transplant survival: interaction of MELD score and hospitalization status.  J Hepatol. 2015;63(3):601-608.PubMedGoogle ScholarCrossref
6.
Klein  KB, Stafinski  TD, Menon  D.  Predicting survival after liver transplantation based on pre-transplant MELD score: a systematic review of the literature.  PLoS One. 2013;8(12):e80661.PubMedGoogle ScholarCrossref
7.
Peetz  A, Salim  A, Askari  R,  et al.  Association of Model for End-Stage Liver Disease score and mortality in trauma patients with chronic liver disease.  JAMA Surg. 2016;151(1):41-48.PubMedGoogle ScholarCrossref
8.
Ghaferi  AA, Birkmeyer  JD, Dimick  JB.  Variation in hospital mortality associated with inpatient surgery.  N Engl J Med. 2009;361(14):1368-1375.PubMedGoogle ScholarCrossref
9.
Havens  JM, Peetz  AB, Do  WS,  et al.  The excess morbidity and mortality of emergency general surgery.  J Trauma Acute Care Surg. 2015;78(2):306-311.PubMedGoogle ScholarCrossref
10.
Akinbami  F, Askari  R, Steinberg  J, Panizales  M, Rogers  SO  Jr.  Factors affecting morbidity in emergency general surgery.  Am J Surg. 2011;201(4):456-462.PubMedGoogle ScholarCrossref
11.
Li  LT, Jafrani  RJ, Becker  NS,  et al.  Outcomes of acute versus elective primary ventral hernia repair.  J Trauma Acute Care Surg. 2014;76(2):523-528.PubMedGoogle ScholarCrossref
12.
Matsuyama  T, Iranami  H, Fujii  K, Inoue  M, Nakagawa  R, Kawashima  K.  Risk factors for postoperative mortality and morbidities in emergency surgeries.  J Anesth. 2013;27(6):838-843.PubMedGoogle ScholarCrossref
13.
To  KB, Cherry-Bukowiec  JR, Englesbe  MJ,  et al.  Emergent versus elective cholecystectomy: conversion rates and outcomes.  Surg Infect (Larchmt). 2013;14(6):512-519.PubMedGoogle ScholarCrossref
14.
Bilimoria  KY, Liu  Y, Paruch  JL,  et al.  Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons.  J Am Coll Surg. 2013;217(5):833-842, e1, e3.PubMedGoogle ScholarCrossref
15.
Havens  JM, Olufajo  OA, Cooper  ZR, Haider  AH, Shah  AA, Salim  A.  Defining rates and risk factors for readmissions following emergency general surgery  [published online November 11, 2015].  JAMA Surg. doi:10.1001/jamasurg.2015.4056.PubMedGoogle Scholar
16.
Hug  BL, Lipsitz  SR, Seger  DL, Karson  AS, Wright  SC, Bates  DW.  Mortality and drug exposure in a 5-year cohort of patients with chronic liver disease.  Swiss Med Wkly. 2009;139(51-52):737-746.PubMedGoogle Scholar
17.
Murphy  SN, Chueh  HC.  A security architecture for query tools used to access large biomedical databases.  Proc AMIA Symp. 2002:552-556.PubMedGoogle Scholar
18.
Nalichowski  R, Keogh  D, Chueh  HC, Murphy  SN.  Calculating the benefits of a Research Patient Data Repository.  AMIA Annu Symp Proc. 2006:1044.PubMedGoogle Scholar
19.
Zager  S, Mendu  ML, Chang  D,  et al.  Neighborhood poverty rate and mortality in patients receiving critical care in the academic medical center setting.  Chest. 2011;139(6):1368-1379.PubMedGoogle ScholarCrossref
20.
Mogensen  KM, Robinson  MK, Casey  JD,  et al.  Nutritional status and mortality in the critically ill.  Crit Care Med. 2015;43(12):2605-2615.PubMedGoogle ScholarCrossref
21.
Malinchoc  M, Kamath  PS, Gordon  FD, Peine  CJ, Rank  J, ter Borg  PC.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.  Hepatology. 2000;31(4):864-871.PubMedGoogle ScholarCrossref
22.
Wiesner  R, Edwards  E, Freeman  R,  et al; United Network for Organ Sharing Liver Disease Severity Score Committee.  Model for End-Stage Liver Disease (MELD) and allocation of donor livers.  Gastroenterology. 2003;124(1):91-96.PubMedGoogle ScholarCrossref
23.
Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.  J Clin Epidemiol. 1992;45(6):613-619.PubMedGoogle ScholarCrossref
24.
Liu  V, Escobar  GJ, Greene  JD,  et al.  Hospital deaths in patients with sepsis from 2 independent cohorts.  JAMA. 2014;312(1):90-92.PubMedGoogle ScholarCrossref
25.
Purtle  SW, Moromizato  T, McKane  CK, Gibbons  FK, Christopher  KB.  The association of red cell distribution width at hospital discharge and out-of-hospital mortality following critical illness*.  Crit Care Med. 2014;42(4):918-929.PubMedGoogle ScholarCrossref
26.
Martin  GS, Mannino  DM, Eaton  S, Moss  M.  The epidemiology of sepsis in the United States from 1979 through 2000.  N Engl J Med. 2003;348(16):1546-1554.PubMedGoogle ScholarCrossref
27.
Braun  AB, Litonjua  AA, Moromizato  T, Gibbons  FK, Giovannucci  E, Christopher  KB.  Association of low serum 25-hydroxyvitamin D levels and acute kidney injury in the critically ill.  Crit Care Med. 2012;40(12):3170-3179.PubMedGoogle ScholarCrossref
28.
Thickett  DR, Moromizato  T, Litonjua  AA,  et al.  Association between prehospital vitamin D status and incident acute respiratory failure in critically ill patients: a retrospective cohort study.  BMJ Open Respir Res. 2015;2(1):e000074.PubMedGoogle ScholarCrossref
29.
Elias  KM, Moromizato  T, Gibbons  FK, Christopher  KB.  Derivation and validation of the acute organ failure score to predict outcome in critically ill patients: a cohort study.  Crit Care Med. 2015;43(4):856-864.PubMedGoogle ScholarCrossref
30.
Sohn  MW, Arnold  N, Maynard  C, Hynes  DM.  Accuracy and completeness of mortality data in the Department of Veterans Affairs.  Popul Health Metr. 2006;4:2.PubMedGoogle ScholarCrossref
31.
Horkan  CM, Purtle  SW, Mendu  ML, Moromizato  T, Gibbons  FK, Christopher  KB.  The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study*.  Crit Care Med. 2015;43(2):354-364.PubMedGoogle ScholarCrossref
32.
Koehler  BE, Richter  KM, Youngblood  L,  et al.  Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle.  J Hosp Med. 2009;4(4):211-218.PubMedGoogle ScholarCrossref
33.
Landrum  L, Weinrich  S.  Readmission data for outcomes measurement: identifying and strengthening the empirical base.  Qual Manag Health Care. 2006;15(2):83-95.PubMedGoogle ScholarCrossref
34.
Jencks  SF, Williams  MV, Coleman  EA.  Rehospitalizations among patients in the Medicare fee-for-service program.  N Engl J Med. 2009;360(14):1418-1428.PubMedGoogle ScholarCrossref
35.
Kamath  PS, Kim  WR; Advanced Liver Disease Study Group.  The Model for End-Stage Liver Disease (MELD).  Hepatology. 2007;45(3):797-805.PubMedGoogle ScholarCrossref
36.
Odom  SR, Gupta  A, Talmor  D, Novack  V, Sagy  I, Evenson  AR.  Emergency hernia repair in cirrhotic patients with ascites.  J Trauma Acute Care Surg. 2013;75(3):404-409.PubMedGoogle ScholarCrossref
37.
Schenker  Y, Fernandez  A, Sudore  R, Schillinger  D.  Interventions to improve patient comprehension in informed consent for medical and surgical procedures: a systematic review.  Med Decis Making. 2011;31(1):151-173.PubMedGoogle ScholarCrossref
38.
Sherman  SK, Hrabe  JE, Charlton  ME, Cromwell  JW, Byrn  JC.  Development of an improved risk calculator for complications in proctectomy.  J Gastrointest Surg. 2014;18(5):986-994.PubMedGoogle ScholarCrossref
39.
Hyder  JA, Reznor  G, Wakeam  E, Nguyen  LL, Lipsitz  SR, Havens  JM.  Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP  [published online December 31, 2015].  Ann Surg. doi:10.1097/SLA.0000000000001558.PubMedGoogle Scholar
40.
Trotter  JF, Olson  J, Lefkowitz  J, Smith  AD, Arjal  R, Kenison  J.  Changes in international normalized ratio (INR) and Model for Endstage Liver Disease (MELD) based on selection of clinical laboratory.  Am J Transplant. 2007;7(6):1624-1628.PubMedGoogle ScholarCrossref
41.
Hall  MH, Esposito  RA, Pekmezaris  R,  et al.  Cardiac surgery nurse practitioner home visits prevent coronary artery bypass graft readmissions.  Ann Thorac Surg. 2014;97(5):1488-1493.PubMedGoogle ScholarCrossref
42.
Shafi  S, Aboutanos  MB, Agarwal  S  Jr,  et al; AAST Committee on Severity Assessment and Patient Outcomes.  Emergency general surgery: definition and estimated burden of disease.  J Trauma Acute Care Surg. 2013;74(4):1092-1097.PubMedGoogle ScholarCrossref
43.
Joynt  KE, Jha  AK.  Thirty-day readmissions: truth and consequences.  N Engl J Med. 2012;366(15):1366-1369.PubMedGoogle ScholarCrossref
44.
Kocher  RP, Adashi  EY.  Hospital readmissions and the Affordable Care Act: paying for coordinated quality care.  JAMA. 2011;306(16):1794-1795.PubMedGoogle ScholarCrossref
45.
Knaus  WA, Draper  EA, Wagner  DP, Zimmerman  JE.  APACHE II: a severity of disease classification system.  Crit Care Med. 1985;13(10):818-829.PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    1 Comment for this article
    EXPAND ALL
    MELD Scoring in Emergency General Surgery: Rising Subtletly to Epitomize Problems
    Kumar Jayant | 1 Department of hepato-pancreato-biliary surgery (HPB), Hammersmith Hospital, Imperial College, London, UK. 2 Faculty of Health and Science, Institute of Learning and Teaching, University of Liverp
    To the Editor:
    All of us wish to thank Havens et al., for their efficient handling of the project studying the association of model for end-stage liver disease Score with mortality in Emergency General Surgery Patients (1). Despite advancement in surgical techniques and perioperative care, the complications are quite high in emergency general surgery (EGS). These are reflected regarding increased rates of morbidity and mortality. These could be because of various disturbances in the physiological milieu of a body either due to present disease status or ongoing co-morbid malady. One of such condition is the chronic liver disease (CLD) which
    impose a higher risk for developing postoperative complications independent of a type of surgery. The enormity of morbidity and mortality squares with the extent of hepatic decompensation (2).
    Studies have shown that anaesthetic drugs have an adverse impact on the liver enzymes levels, which could be of little significance in a healthy individual. However, in patients with CLD these insults may precipitate hepatic decompensation (3). In patients with CLD, Model for End-Stage Liver Disease (MELD) score has been evinced to correspond with the preoperative risk. This linear regression model has been designed on serum bilirubin and creatinine levels and international normalised ratio (INR). It is more objective and weights the variables, thereby even a slight increase in the MELD score makes an incremental input into the menace (4). Even though this system was moulded to foresee mortality following TIPS, later it was also enacted to stratify patients ahead of liver transplantation, to predict perioperative mortality post-transplant. Lately, researchers have found a notable link allying MELD score and mortality in trauma patients (5).
    The published works of literature on this topic are based on retrospective cohorts though they have described a consistent increase in operative risk with CLD. However, there is a lack of any meta-analysis or systemic review. These cultivating evinces and inefficiency of existent models, e.g. the American College of Surgeons National Surgical Quality Improvement Project Surgical Risk Calculator, connote a complete openness for MELD score; that may be indispensable in envisioning outcomes in a number of patients not going through a liver transplant procedures (6).

    The study by Havens et al., have described that MELD score is kindred with mortality following intensive care unit admission in the group of emergency general surgery patients with CLD. In this multicenter trial of approximately 700 patients, they have valued the 90 days predictability of MELD score in such patients. Furthermore, they have adumbrated that decreases in MELD scores after 48 hours following intensive care unit admission has a positive impact on outcomes (1).
    There is no doubt that this prospective study by has further added our understanding regarding management of EGS patients with CLD, and thus we are grateful to them for their efforts, however, few points deserve mention (7).
    First, is that the inclusion of the Deyo-Charlson index which has been widely used to assess the burden of chronic illness and predict outcomes. A study by Poses et al. have valued its importance in prognostication in ICU patients by adding them to a more physiological system such as Acute Physiology and Chronic Health Evaluation II (APACHE II) (8). However in a very landmark study by Quach et al., have reported the limited role of the Deyo-Charlson index in predicting mortality in ICU patients (9). However, this index may have practical applications when another physiological scoring system has not been prospectively determined. Studies have outlined the importance of Charlson index in the assessment of outcomes beyond hospital discharge in non-ICU settings thus further studies are needed to explore the ability of this scoring system in ICU patients.
    The second salient point to mention is the need for analysing predictability of MELD scoring system against other prognostic scores as APACHE II), Sequential organ failure assessment (SOFA), Child-Turcotte-Pugh (CTP). Referencing the study by Dusseja et al., over 100 patients with acute on chronic liver disease, which showed that APACHE II scoring system is superior to other prognostic scores in predicting its short-term mortality (10). Here I like to quote a prospective study by Theocharidou et al., 2014 involving a cohort of 635 patients with cirrhosis admitted to ICU. Where they have developed a Royal Free Hospital (RFH), a score for disease prognostication and mortality prediction and compared it against other prognostic models like APACHE II, MELD, CTP and Chronic Liver Failure-Sequential Organ Failure Assessment (CLIF-SOFA) model. Although they have reported good discriminative ability and calibration like other though this also needs further external validation (11).
    The third point in the panel of this commentary is making allowance for the role serum lactate and standard base deficit in the praxis scoring system of CLD patients admitted to the intensive care unit is useful for risk assessment, prognostication and foretelling in-hospital mortality (12). By the same token, need and duration of mechanical ventilation, hypoalbuminemia, anaemia, blood transfusion, the length of hospital stay are few other factors which are laid in various studies as a contributor of poorer long-term survival (13).
    The quintessential requirements that ideal prognostication model should fulfil are; it must be established on easily measurable parameters, non-invasive, clinically sound, and its validity should be generalizable to a variegated congregation of population. Albeit, many groups have put forwarded various systems with promising internal outcomes, however, external validation with reference to aetiology, gender, ethnicity, geography is lacking in the real world.
    Since the appearance of the MELD modus operandi in 2001, MELD has been legitimated and enacted to a vast majority of clinical situations encountered by CLD patients. The enforcement of MELD scoring to prioritise donor livers for transplant in 2002 had called forth a diminution in waiting list registrations and scaled down the mortality on the waiting list without grieving post-transplant outcomes. The MELD score helps clinicians to risk stratifying various interventions on a daily basis in patients with CLD in addition to influencing treatment options (14). The MELD scoring system does have its foibles, and require further reinforcement by other measures of liver, or global functioning is imperative to boost may its prognostic accuracy in the CLD patient undergoing general surgery.

    Bibliography:
    1. HavensJM, ColumbusAB, OlufajoOA, AskariR, Salim A CK. Association of Model for End-Stage Liver Disease Score With Mortality in Emergency General Surgery Patients. 2016;151(7):1–7.
    2. Havens JM, Peetz AB, Do WS, Cooper Z, Kelly E, Askari R, et al. The excess morbidity and mortality of emergency general surgery. J Trauma Acute Care Surg. 2015;78(2):306–11.
    3. Gholson CF, Provenza JM, Bacon BR. Hepatologic considerations in patients with parenchymal liver disease undergoing surgery. Am J Gastroenterol. 1990;85(5):487–96.
    4. O’Leary JG, Friedman LS. Predicting surgical risk in patients with cirrhosis: from art to science. Vol. 132, Gastroenterology. United States; 2007. p. 1609–11.
    5. Peetz A, Salim A, Askari R, De Moya MA, Olufajo OA, Simon TG, et al. Association of Model for End-Stage Liver Disease Score and Mortality in Trauma Patients With Chronic Liver Disease. JAMA Surg. 2016 Jan;151(1):41–8.
    6. Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: A decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5).
    7. Fong ZV, McMillan MT, Marchegiani G, Sahora K, Malleo G, De Pastena M, et al. Discordance Between Perioperative Antibiotic Prophylaxis and Wound Infection Cultures in Patients Undergoing Pancreaticoduodenectomy. JAMA Surg [Internet]. 2015; Available from: http://www.ncbi.nlm.nih.gov/pubmed/26720272
    8. Poses RM, McClish DK, Smith WR, Bekes C, Scott WE. Prediction of survival of critically ill patients by admission comorbidity. J Clin Epidemiol. 1996;49(7):743–7.
    9. Quach S, Hennessy D a, Faris P, Fong A, Quan H, Doig C. A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients. BMC Health Serv Res. 2009;9:129.
    10. Duseja A, Choudhary NS, Gupta S, Dhiman RK, Chawla Y. APACHE II score is superior to SOFA, CTP and MELD in predicting the short-term mortality in patients with acute-on-chronic liver failure (ACLF). J Dig Dis. 2013 Sep;14(9):484–90.
    11. Theocharidou E, Pieri G, Mohammad AO, Cheung M. The Royal Free Hospital Score : A Calibrated Prognostic Model for Patients With Cirrhosis Admitted to Intensive Care Unit . Comparison With Current Models and CLIF-SOFA Score. Am J Gastroenterol [Internet]. 2014;109(4):554–62. Available from: http://dx.doi.org/10.1038/ajg.2013.466
    12. Tobias AZ, Guyette FX, Seymour CW, Suffoletto BP, Martin-Gill C, Quintero J, et al. Pre-resuscitation Lactate and Hospital Mortality in Prehospital Patients. Prehospital Emerg Care [Internet]. 2014;18(3):321–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24548128
    13. Han ML, Chen CC, Kuo SH, Hsu WF, Liou JM, Wu MS, et al. Predictors of in-hospital mortality after acute variceal bleeding in patients with hepatocellular carcinoma and concurrent main portal vein thrombosis. J Gastroenterol Hepatol. 2014;29(2):344–51.
    14. Kamath PS, Kim WR. The model for end-stage liver disease (MELD). Hepatology [Internet]. 2007;45(3):797–805. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17326206







    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    Pacific Coast Surgical Association
    July 20, 2016

    Association of Model for End-Stage Liver Disease Score With Mortality in Emergency General Surgery Patients

    Author Affiliations
    • 1Center for Surgery and Public Health, Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
    • 2Division of Trauma, Burns, and Surgical Critical Care, Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
    • 3The Nathan E. Hellman Memorial Laboratory, Renal Division, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    JAMA Surg. 2016;151(7):e160789. doi:10.1001/jamasurg.2016.0789
    Abstract

    Importance  Emergency general surgery (EGS) patients have a disproportionate burden of death and complications. Chronic liver disease (CLD) increases the risk of complications following elective surgery. For EGS patients with CLD, long-term outcomes are unknown and risk stratification models do not reflect severity of CLD.

    Objective  To determine whether the Model for End-Stage Liver Disease (MELD) score is associated with increased risk of 90-day mortality following intensive care unit (ICU) admission in EGS patients.

    Design, Setting, and Participants  We performed a retrospective cohort study of patients with CLD who underwent an EGS procedure based on International Classification of Diseases, Ninth Revision (ICD-9) procedure codes and were admitted to a medical or surgical ICU within 48 hours of surgery between January 1, 1998, and September 20, 2012, at 2 academic medical centers. Chronic liver disease was identified using ICD-9 codes. Multivariable logistic regression was performed. The analysis was conducted from July 1, 2015, to January 1, 2016.

    Main Outcomes and Measures  The primary outcome was all-cause 90-day mortality.

    Results  A total of 13 552 EGS patients received critical care; of these, 707 (5%) (mean [SD] age at hospital admission, 56.6 [14.2] years; 64% male; 79% white) had CLD and data to determine MELD score at ICU admission. The median MELD score was 14 (interquartile range, 10-20). Overall 90-day mortality was 30.1%. The adjusted odds ratio of 90-day mortality for each 10-point increase in MELD score was 1.63 (95% CI, 1.34-1.98). A decrease in MELD score of more than 3 in the 48 hours following ICU admission was associated with a 2.2-fold decrease in 90-day mortality (odds ratio = 0.46; 95% CI, 0.22-0.98).

    Conclusions and Relevance  In this study, MELD score was associated with 90-day mortality following EGS in patients with CLD. The MELD score can be used as a prognostic factor in this patient population and should be used in preoperative risk prediction models and when counseling EGS patients on the risks and benefits of operative intervention.

    Introduction

    Cirrhosis and chronic liver disease (CLD) are significant causes of morbidity and mortality in the United States, with CLD accounting for 36 427 deaths among hospitalized patients in 2013.1 Patients with CLD undergoing surgery have comparatively higher rates of surgical complications and death.2 In addition, among patients with CLD, admission to the intensive care unit (ICU), mechanical ventilation, and renal replacement therapy have been independently shown to increase hospital mortality.3,4 Scoring tools such as the Model for End-Stage Liver Disease (MELD) score are used to predict outcomes in patients with CLD.2-6 However, most of these studies are restricted to patients who underwent liver transplantation, and most studies among patients not receiving a transplant have not focused on care delivered in emergent settings. Because patients with CLD also experience acute surgical events,7 it is important to examine and accurately predict the outcomes of acute surgical care among these patients.

    Emergency general surgery (EGS) is associated with increased rates of morbidity and mortality compared with nonemergent general surgery cases.8 Patients undergoing EGS are approximately 2.5 times more likely to experience a significant complication and have a 6-fold increase in mortality relative to non-EGS patients.9 The underlying causes of this increased morbidity and mortality are not fully understood, but medical comorbidities and physiological derangements are likely to be contributing factors.10-13 Although surgical risk calculation tools such as the American College of Surgeons National Surgical Quality Improvement Project Surgical Risk Calculator are used to gain an objective sense of surgical risk stratification, such tools have yet to be comprehensively studied in this patient population and do not include the use of liver disease–specific assessment tools such as the MELD score in the prediction of outcomes among patients with CLD undergoing EGS.14 We hypothesized that among patients with CLD who underwent EGS and were treated in the ICU, the MELD score would independently be associated with mortality. Therefore, the aim of this study was to determine whether the MELD score is associated with increased risk of 90-day mortality following ICU admission among EGS patients with CLD.

    Box Section Ref ID

    Key Points

    • Question What is the relationship between Model for End-Stage Liver Disease (MELD) score and mortality in emergency general surgery patients?

    • Findings In this cohort study that included 707 emergency general surgery patients with chronic liver disease admitted to a surgical intensive care unit, MELD score was independently associated with 90-day mortality. A decrease in MELD score after 48 hours was associated with improved survival.

    • Meaning The MELD score is an important prognostic tool for emergency general surgery patients with chronic liver disease.

    Methods

    This is a retrospective cohort study of patients admitted between January 1, 1998, and September 20, 2012, to 2 large academic medical centers that provide primary and tertiary care to an ethnically and socioeconomically diverse population within eastern Massachusetts and the surrounding region. Patients aged 18 years or older who required ICU admission and had emergency surgery (previously defined by the American Association for the Surgery of Trauma15) within 48 hours of ICU admission were included. Patients without CLD16 or without information available for MELD score calculation at ICU admission were excluded. Data were obtained through the Research Patient Data Registry (RPDR), a computerized registry that serves as a central data warehouse for all inpatient and outpatient records at Partners HealthCare sites.17,18 The RPDR has been previously used in clinical research studies in critically ill patients.7,19,20 Approval for the study was granted by the Partners Human Research Committee. Requirement for informed consent was waived as the data were analyzed anonymously.

    The exposure of interest was the MELD score at ICU admission.21 The MELD score was calculated at ICU admission using United Network for Organ Sharing modifications as follows: MELD Score = [0.957 × ln(Serum Creatinine) + 0.378 × ln(Serum Bilirubin) + 1.120 × ln(INR) + 0.643) × 10], where serum creatinine and serum bilibrubin are in milligrams per deciliter and INR indicates international normalized ratio; if the patient is undergoing hemodialysis, the value for serum creatinine is automatically set to 4.0 mg/dL. Because the MELD score uses a log scale calculation, any creatinine, bilirubin, or INR value less than 1 is given the lower limit value of 1 to prevent a negative score.22 The MELD score was categorized as 6.0 to 9.9; 10.0 to 19.9; 20.0 to 29.9; and 30.0 or higher. Chronic liver disease was identified by the presence of International Classification of Diseases, Ninth Revision (ICD-9) codes for CLD (571.x), chronic hepatitis (70.54), or chronic hepatitis B (70.32) prior to or during hospital admission. This approach had been previously validated using the RPDR data set.16 The Deyo-Charlson index was used to assess the burden of chronic illness with higher scores indicating more comorbidity via ICD-9 coding algorithms, a well-studied and validated approach.23 Race was designated by the patient or by a patient representative.

    Sepsis was defined by ICD-9 codes 038, 995.91, 995.92, or 785.52, 3 days prior to critical care initiation to 7 days after critical care initiation.24 Using electronic pharmacy records, exposure to inotropes and vasopressors was determined for dopamine hydrochloride, dobutamine hydrochloride, epinephrine, norepinephrine bitartrate, phenylephrine hydrochloride, milrinone, and vasopressin. Inotropes or vasopressors were considered to be present if prescribed 3 days prior to critical care initiation to 7 days after critical care initiation.25 Acute organ failure was adapted from the study by Martin et al26 and defined by a combination of ICD-9 and Current Procedural Terminology (CPT) codes relating to acute organ dysfunction (respiratory failure, cardiovascular failure, renal, hepatic, hematologic, metabolic, and/or neurologic) assigned from 3 days prior to critical care initiation to 30 days after critical care initiation.

    Acute kidney injury was defined as Risk, Injury, Failure, Loss, and End-Stage Kidney Disease (RIFLE) class injury or failure occurring between 3 days prior to critical care initiation and 7 days after critical care initiation.27 Noncardiogenic acute respiratory failure was identified by the presence of ICD-9 codes for respiratory failure or pulmonary edema (518.4, 518.5, 518.81, and 518.82) and mechanical ventilation (96.7x), excluding congestive heart failure (428.0-428.9), following hospital admission.28 For severity of illness risk adjustment, we used the acute organ failure score, an ICU risk prediction score derived and validated from demographic characteristics (age, race) as well as ICD-9, Clinical Modification code–based comorbidity, sepsis, and acute organ failure covariates that has similar discrimination for 30-day mortality as the Acute Physiologic and Chronic Health Evaluation II score.29 All CPT or ICD-9 codes were derived from daily billing charges from individual physicians.

    End Points

    The primary end point was 90-day all-cause mortality following critical care initiation. We used the Social Security Administration Death Master File to determine vital status, which has high sensitivity and specificity for mortality.30 We have validated the accuracy of the Social Security Administration Death Master File for in-hospital and out-of-hospital mortality in the RPDR database.19 Among the cohort, 100% had at least 90-day follow-up after ICU admission. The censoring date was December 31, 2012. The secondary end point was 30-day hospital readmission, which was determined from RPDR hospital admission data as previously described31 and was defined as a subsequent or unscheduled admission to Brigham and Women’s Hospital or Massachusetts General Hospital within 30 days of discharge following the hospitalization associated with the critical care exposure.31-33 We excluded readmissions with diagnosis related group codes that are commonly associated with planned readmissions in addition to diagnosis related group codes for transplantation, procedures related to pregnancy, and psychiatric issues.31,34

    Statistical Analysis

    Based on prior studies,7,35 we assumed that 90-day mortality would increase an absolute 17.7% in patients with a MELD score of 20 to 29 (25%) compared with those with a MELD score lower than 9.9 (12.5%). With an α error level of 5% and a power of 80%, the minimum sample size thus required for our primary end point was 336 patients.

    Categorical covariates were described by frequency distribution and compared across MELD score groups using contingency tables and χ2 testing. Continuous covariates were examined graphically and in terms of summary statistics and compared across MELD groups using 1-way analysis of variance. Unadjusted associations between MELD groups and outcomes were estimated by bivariable logistic regression analysis. Adjusted odds ratios (ORs) were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly interact with both MELD and mortality.

    Overall model fit was assessed using the Hosmer-Lemeshow test. Analyses based on fully adjusted models were performed to evaluate the MELD-mortality association, and P for interaction was determined to explore for any evidence of effect modification. To evaluate for multicollinearity, we calculated the variance inflation factors and tolerances for each of the independent variables. Locally weighted scatterplot smoothing was used to graphically represent the relationship between absolute MELD count and rate of 90-day mortality. All P values presented are 2-tailed; P < .05 was considered nominally significant. All analyses were performed using Stata version 13.1 MP statistical software (StataCorp LP).

    Results

    Between 1998 and 2012, there were 13 552 unique EGS patients treated in the ICU. A total of 12 623 patients without CLD and 222 patients without information available for MELD score calculation at ICU admission were excluded, leaving 707 patients in the final cohort.

    In the study cohort, the mean (SD) age at hospital admission was 56.6 (14.2) years. Most patients were male (64%) and white (79%). The in-hospital, 90-day, and 365-day mortality rates were 25.3%, 30.1%, and 41.1%, respectively. The rate of unplanned 30-day postdischarge hospital readmissions was 16.1%. The median MELD score was 14 (interquartile range, 10-20). Patient characteristics of the study cohort were stratified according to 90-day mortality (Table 1). Age, Deyo-Charlson index score, acute organ failure, sepsis, intubation, use of vasopressors and inotropes, metastatic malignant neoplasm, renal replacement therapy, and MELD score were significantly associated with 90-day mortality. Factors that were associated with stratified MELD category included age, race, Deyo-Charlson index score, acute organ failure, sepsis, use of vasopressors and inotropes, renal replacement therapy, glomerular filtration rate, days from hospital admission to ICU admission, and mortality (Table 2).

    Mortality risk in the 90 days after ICU admission was higher in patients with higher MELD scores (Figure). Compared with patients with a MELD score of 6.0 to 9.9, the odds of 90-day mortality were 2.0-fold higher in those with a MELD score of 10.0 to 19.9 (OR = 1.99; 95% CI, 1.25-3.16), 3.5-fold higher in those with a MELD score of 20.0 to 29.9 (OR = 3.49; 95% CI, 2.04-5.97), and 5.2-fold higher in those with a MELD score of 30.0 or higher (OR = 5.25; 95% CI, 2.79-9.88) (Table 3). The MELD score level remained a significant predictor of the odds of 90-day mortality after adjustment for age, sex, race, Deyo-Charlson index score, sepsis, and acute organ failure. Again compared with patients with a MELD score of 6.0 to 9.9, the adjusted odds of 90-day mortality were 1.4-fold higher (but not statistically significant) in those with a MELD score of 10.0 to 19.9 (OR = 1.45; 95% CI, 0.89-2.38), 2.1-fold higher in those with a MELD score of 20.0 to 29.9 (OR = 2.12; 95% CI, 1.17-3.85), and 3.6-fold higher in those with a MELD score of 30.0 or higher (OR = 3.58; 95% CI, 1.76-7.28) (Table 3).

    The adjusted 90-day mortality model showed good calibration (Hosmer-Lemeshow χ28 = 12.22; P = .14), good discrimination (C statistic = 0.73; 95% CI, 0.69-0.77), and an absence of multicollinearity as determined by variance inflation factor. When MELD score was analyzed as continuous, the adjusted OR of 90-day mortality for each 10-point increase in MELD score was 1.63 (95% CI, 1.34-1.98). Further, the hazard ratios of mortality adjusted for age, sex, race, Deyo-Charlson index score, sepsis, and acute organ failure were 1.25 (95% CI, 0.93-1.68) for patients with a MELD score of 10.0 to 19.9, 1.63 (95% CI, 1.14-2.34) for those with a MELD score of 20.0 to 29.9, and 1.81 (95% CI, 1.16-2.81) for those with a MELD score of 30.0 or higher compared with patients with a MELD score of 6.0 to 9.9.

    There was no significant effect modification of the association between MELD score and 90-day mortality on the basis of acute kidney injury (P for interaction = .55), hospital (P for interaction = .26), or year of ICU admission (P for interaction = .08). Effect modification is present regarding the presence of sepsis (P for interaction = .007). Individually running the final model with and without a sepsis term to the final model does not alter the effect size or significance of the change in the association between MELD score and 90-day mortality (data not shown).

    Elevated MELD score was a predictor of 30-day hospital readmission in ICU survivors with CLD who underwent EGS (n = 528). The odds of 30-day hospital readmission in patients with a MELD score higher than 19.9 was 1.7-fold that of patients with a MELD score of 19.9 or lower (OR = 1.73; 95% CI, 1.07-2.77; P = .02). The presence of a MELD score higher than 19.9 remained a significant predictor of the odds of 30-day hospital readmission after adjustment for age, sex, race, Deyo-Charlson index score, acute organ failure, and sepsis. The adjusted odds of 30-day hospital readmission in patients with a MELD score higher than 19.9 was 1.7-fold that of patients with a MELD score of 19.9 or lower (OR = 1.71; 95% CI, 1.01-2.92; P = .047).

    In a subset of patients with MELD score determined at ICU admission and 48 hours after (n = 318), we determined the association between a change in MELD score and 90-day mortality. A decrease in MELD score of more than 3 in the 48 hours following ICU admission was associated with a 2.2-fold decrease in 90-day mortality (OR = 0.46; 95% CI, 0.22-0.98; P = .045) relative to patients with a change in MELD score of ±3 adjusted for age, sex, race, sepsis, Deyo-Charlson index score, and acute organ failure. An increase in MELD score of more than 3 in the 48 hours following ICU admission was associated with a nonsignificant increase in 90-day mortality (OR = 1.40; 95% CI, 0.77-2.54; P = .27), fully adjusted, compared with patients with a change of ±3 in MELD score.

    Discussion

    In this study of patients with CLD who underwent EGS, we demonstrated that increasing severity of CLD was associated with increased odds of 90-day mortality and that a decrease in MELD score after 48 hours was associated with significant reductions in the odds of 90-day mortality. This study also showed that 1 in 6 EGS patients with CLD will be readmitted. This is the first study, to our knowledge, to demonstrate this relationship in the EGS patient.

    The MELD score, first used to predict survival in patients undergoing transjugular intrahepatic portosystemic shunts,21 has been used extensively in the United States and Europe as a tool for the prioritization of liver transplants.22 More recently, researchers have identified an association between MELD score and mortality in trauma patients7 and major complications in cirrhotic patients undergoing emergency hernia repair.36 This growing body of evidence suggests that MELD scores may be important in predicting outcomes among a wide variety of patients not receiving a transplant.

    Emergency general surgery patients have a very high burden of complications and death.8-11 Appropriate methods are therefore necessary to accurately risk stratify these patients and help predict their outcomes and to identify ICU survivors at high risk for adverse outcomes following hospital discharge. Understanding the true risk of death following surgery is critical to the shared decision-making process for both surgeons and patients.37 Current surgical risk calculators either do not include liver disease or do not include severity of liver disease as measured by laboratory data in mortality calculations.38 One common surgical risk assessment tool, the American College of Surgeons National Surgical Quality Improvement Project Surgical Risk Calculator, includes the presence of ascites within 30 days preoperatively as a universal risk factor that may relate to liver disease or to other factors.14 However, this tool has been shown to underestimate mortality in EGS patients.39 Inclusion of the MELD score in surgical risk calculators may improve accuracy and aid in patient counseling along with operative decision making.

    In this study, we identified an association between improvement in MELD score at 48 hours and improved survival. This association has also been described in trauma patients.7 As there are 3 laboratory components to the MELD score (serum bilirubin level, serum creatinine level, and INR), the association between a change in MELD score and 90-day mortality may reflect changes in liver or renal function in the first 48 hours of critical care. Among the 3 variables of the MELD score, INR has the highest multiplicative value, followed by creatinine level. Variations in INR or creatinine level may translate to up to 20% differences in MELD score.40 As this was an observational study, we cannot conclude that survival can be improved by focused attention to improving laboratory values. However, we do believe that a patient’s clinical change as reflected in changes in laboratory values such as INR or serum creatinine level is reflected in both MELD score and outcomes such as survival. While these variables likely relate to clinical changes that may be seen in all patients, not just those with CLD, it is unknown whether MELD score would be associated with mortality in patients without CLD.

    This study is in agreement with prior studies showing high risk for 30-day readmission following EGS.15 Emergency general surgery patients identified as having elevated MELD scores at ICU admission may benefit from increased follow-up after hospital discharge to prevent unplanned readmission. Efforts to improve continuity between inpatient and outpatient care in the form of improved communication between physicians and trained home nursing visits have been shown to reduce readmission rates in surgical patient populations.41 Further investigation into the role of such services in EGS patients, along with other surgical cohorts, is warranted.

    One of the strengths of this study is that unlike prior evaluations of MELD scores that include few patients at a single center, we used a large, prospectively designed intensive care database that comprised patients treated at different hospitals and contained both administrative and clinical data. This offered us a large sample size and better ability to control for clinical conditions than purely administrative databases. Also, in addition to the previously validated approaches to define EGS,15,42 CLD,16 comorbidites,23 acute organ failure,29 and sepsis,24 we determined all-cause mortality using a validated method based on the Social Security Administration Death Master File.19 Very few studies are able to determine patients’ survival status once they are discharged from the hospital. Last, we examined 30-day readmission rates, which have become a quality metric used in evaluating the care of Medicare patients in the United States.43,44

    However, the limitations of this study must also be considered. Ascertainment bias may be present as more than one-fifth of EGS patients with CLD had missing data on the MELD components, potentially limiting generalizability of our study findings. Residual confounding may also be present despite multivariable adjustment. We were unable to adjust for physiologically based severity of illness scores, which are strong predictors of critical illness outcome.45 It is conceivable that inclusion of a physiological score in the analysis may alter the associations between MELD score and outcomes.

    Conclusions

    These data demonstrate that in critically ill patients with CLD, increased MELD score is associated with increased mortality and hospital readmission following EGS. Concurrently, MELD score reduction exerts a protective effect in this patient population. The identification of exposures that are predictive of outcomes in EGS patients may be useful for preoperative planning and may be useful inclusions for future risk stratification models. Furthermore, EGS patients with CLD might benefit from enhanced longitudinal care following hospital discharge to reduce unplanned readmissions. Further investigation into the relationship between MELD score and outcomes in patients without CLD is warranted.

    Back to top
    Article Information

    Corresponding Author: Joaquim M. Havens, MD, Division of Trauma, Burns, and Surgical Critical Care, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115 (jhavens@partners.org).

    Accepted for Publication: March 11, 2016.

    Published Online: May 18, 2016. doi:10.1001/jamasurg.2016.0789.

    Author Contributions: Dr Christopher had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: Havens, Askari, Salim, Christopher.

    Acquisition, analysis, or interpretation of data: Columbus, Olufajo, Christopher.

    Drafting of the manuscript: Havens, Columbus, Olufajo, Christopher.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Christopher.

    Administrative, technical, or material support: Havens, Askari.

    Study supervision: Havens, Salim.

    Conflict of Interest Disclosures: None reported.

    Previous Presentation: This paper was presented at the 87th Annual Meeting of the Pacific Coast Surgical Association; February 16, 2016; Kohala Coast, Hawaii.

    Additional Contributions: This article is dedicated to the memory of our dear friend and colleague Nathan Edward Hellman, MD, PhD.

    References
    1.
    Centers for Disease Control and Prevention. Chronic liver disease and cirrhosis. http://www.cdc.gov/nchs/fastats/liver-disease.htm. Accessed December 21, 2015.
    2.
    Suman  A, Carey  WD.  Assessing the risk of surgery in patients with liver disease.  Cleve Clin J Med. 2006;73(4):398-404.PubMedGoogle ScholarCrossref
    3.
    Levesque  E, Hoti  E, Azoulay  D,  et al.  Prospective evaluation of the prognostic scores for cirrhotic patients admitted to an intensive care unit.  J Hepatol. 2012;56(1):95-102.PubMedGoogle ScholarCrossref
    4.
    Bosetti  C, Levi  F, Lucchini  F, Zatonski  WA, Negri  E, La Vecchia  C.  Worldwide mortality from cirrhosis: an update to 2002.  J Hepatol. 2007;46(5):827-839.PubMedGoogle ScholarCrossref
    5.
    Bittermann  T, Makar  G, Goldberg  DS.  Early post-transplant survival: interaction of MELD score and hospitalization status.  J Hepatol. 2015;63(3):601-608.PubMedGoogle ScholarCrossref
    6.
    Klein  KB, Stafinski  TD, Menon  D.  Predicting survival after liver transplantation based on pre-transplant MELD score: a systematic review of the literature.  PLoS One. 2013;8(12):e80661.PubMedGoogle ScholarCrossref
    7.
    Peetz  A, Salim  A, Askari  R,  et al.  Association of Model for End-Stage Liver Disease score and mortality in trauma patients with chronic liver disease.  JAMA Surg. 2016;151(1):41-48.PubMedGoogle ScholarCrossref
    8.
    Ghaferi  AA, Birkmeyer  JD, Dimick  JB.  Variation in hospital mortality associated with inpatient surgery.  N Engl J Med. 2009;361(14):1368-1375.PubMedGoogle ScholarCrossref
    9.
    Havens  JM, Peetz  AB, Do  WS,  et al.  The excess morbidity and mortality of emergency general surgery.  J Trauma Acute Care Surg. 2015;78(2):306-311.PubMedGoogle ScholarCrossref
    10.
    Akinbami  F, Askari  R, Steinberg  J, Panizales  M, Rogers  SO  Jr.  Factors affecting morbidity in emergency general surgery.  Am J Surg. 2011;201(4):456-462.PubMedGoogle ScholarCrossref
    11.
    Li  LT, Jafrani  RJ, Becker  NS,  et al.  Outcomes of acute versus elective primary ventral hernia repair.  J Trauma Acute Care Surg. 2014;76(2):523-528.PubMedGoogle ScholarCrossref
    12.
    Matsuyama  T, Iranami  H, Fujii  K, Inoue  M, Nakagawa  R, Kawashima  K.  Risk factors for postoperative mortality and morbidities in emergency surgeries.  J Anesth. 2013;27(6):838-843.PubMedGoogle ScholarCrossref
    13.
    To  KB, Cherry-Bukowiec  JR, Englesbe  MJ,  et al.  Emergent versus elective cholecystectomy: conversion rates and outcomes.  Surg Infect (Larchmt). 2013;14(6):512-519.PubMedGoogle ScholarCrossref
    14.
    Bilimoria  KY, Liu  Y, Paruch  JL,  et al.  Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons.  J Am Coll Surg. 2013;217(5):833-842, e1, e3.PubMedGoogle ScholarCrossref
    15.
    Havens  JM, Olufajo  OA, Cooper  ZR, Haider  AH, Shah  AA, Salim  A.  Defining rates and risk factors for readmissions following emergency general surgery  [published online November 11, 2015].  JAMA Surg. doi:10.1001/jamasurg.2015.4056.PubMedGoogle Scholar
    16.
    Hug  BL, Lipsitz  SR, Seger  DL, Karson  AS, Wright  SC, Bates  DW.  Mortality and drug exposure in a 5-year cohort of patients with chronic liver disease.  Swiss Med Wkly. 2009;139(51-52):737-746.PubMedGoogle Scholar
    17.
    Murphy  SN, Chueh  HC.  A security architecture for query tools used to access large biomedical databases.  Proc AMIA Symp. 2002:552-556.PubMedGoogle Scholar
    18.
    Nalichowski  R, Keogh  D, Chueh  HC, Murphy  SN.  Calculating the benefits of a Research Patient Data Repository.  AMIA Annu Symp Proc. 2006:1044.PubMedGoogle Scholar
    19.
    Zager  S, Mendu  ML, Chang  D,  et al.  Neighborhood poverty rate and mortality in patients receiving critical care in the academic medical center setting.  Chest. 2011;139(6):1368-1379.PubMedGoogle ScholarCrossref
    20.
    Mogensen  KM, Robinson  MK, Casey  JD,  et al.  Nutritional status and mortality in the critically ill.  Crit Care Med. 2015;43(12):2605-2615.PubMedGoogle ScholarCrossref
    21.
    Malinchoc  M, Kamath  PS, Gordon  FD, Peine  CJ, Rank  J, ter Borg  PC.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.  Hepatology. 2000;31(4):864-871.PubMedGoogle ScholarCrossref
    22.
    Wiesner  R, Edwards  E, Freeman  R,  et al; United Network for Organ Sharing Liver Disease Severity Score Committee.  Model for End-Stage Liver Disease (MELD) and allocation of donor livers.  Gastroenterology. 2003;124(1):91-96.PubMedGoogle ScholarCrossref
    23.
    Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.  J Clin Epidemiol. 1992;45(6):613-619.PubMedGoogle ScholarCrossref
    24.
    Liu  V, Escobar  GJ, Greene  JD,  et al.  Hospital deaths in patients with sepsis from 2 independent cohorts.  JAMA. 2014;312(1):90-92.PubMedGoogle ScholarCrossref
    25.
    Purtle  SW, Moromizato  T, McKane  CK, Gibbons  FK, Christopher  KB.  The association of red cell distribution width at hospital discharge and out-of-hospital mortality following critical illness*.  Crit Care Med. 2014;42(4):918-929.PubMedGoogle ScholarCrossref
    26.
    Martin  GS, Mannino  DM, Eaton  S, Moss  M.  The epidemiology of sepsis in the United States from 1979 through 2000.  N Engl J Med. 2003;348(16):1546-1554.PubMedGoogle ScholarCrossref
    27.
    Braun  AB, Litonjua  AA, Moromizato  T, Gibbons  FK, Giovannucci  E, Christopher  KB.  Association of low serum 25-hydroxyvitamin D levels and acute kidney injury in the critically ill.  Crit Care Med. 2012;40(12):3170-3179.PubMedGoogle ScholarCrossref
    28.
    Thickett  DR, Moromizato  T, Litonjua  AA,  et al.  Association between prehospital vitamin D status and incident acute respiratory failure in critically ill patients: a retrospective cohort study.  BMJ Open Respir Res. 2015;2(1):e000074.PubMedGoogle ScholarCrossref
    29.
    Elias  KM, Moromizato  T, Gibbons  FK, Christopher  KB.  Derivation and validation of the acute organ failure score to predict outcome in critically ill patients: a cohort study.  Crit Care Med. 2015;43(4):856-864.PubMedGoogle ScholarCrossref
    30.
    Sohn  MW, Arnold  N, Maynard  C, Hynes  DM.  Accuracy and completeness of mortality data in the Department of Veterans Affairs.  Popul Health Metr. 2006;4:2.PubMedGoogle ScholarCrossref
    31.
    Horkan  CM, Purtle  SW, Mendu  ML, Moromizato  T, Gibbons  FK, Christopher  KB.  The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study*.  Crit Care Med. 2015;43(2):354-364.PubMedGoogle ScholarCrossref
    32.
    Koehler  BE, Richter  KM, Youngblood  L,  et al.  Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle.  J Hosp Med. 2009;4(4):211-218.PubMedGoogle ScholarCrossref
    33.
    Landrum  L, Weinrich  S.  Readmission data for outcomes measurement: identifying and strengthening the empirical base.  Qual Manag Health Care. 2006;15(2):83-95.PubMedGoogle ScholarCrossref
    34.
    Jencks  SF, Williams  MV, Coleman  EA.  Rehospitalizations among patients in the Medicare fee-for-service program.  N Engl J Med. 2009;360(14):1418-1428.PubMedGoogle ScholarCrossref
    35.
    Kamath  PS, Kim  WR; Advanced Liver Disease Study Group.  The Model for End-Stage Liver Disease (MELD).  Hepatology. 2007;45(3):797-805.PubMedGoogle ScholarCrossref
    36.
    Odom  SR, Gupta  A, Talmor  D, Novack  V, Sagy  I, Evenson  AR.  Emergency hernia repair in cirrhotic patients with ascites.  J Trauma Acute Care Surg. 2013;75(3):404-409.PubMedGoogle ScholarCrossref
    37.
    Schenker  Y, Fernandez  A, Sudore  R, Schillinger  D.  Interventions to improve patient comprehension in informed consent for medical and surgical procedures: a systematic review.  Med Decis Making. 2011;31(1):151-173.PubMedGoogle ScholarCrossref
    38.
    Sherman  SK, Hrabe  JE, Charlton  ME, Cromwell  JW, Byrn  JC.  Development of an improved risk calculator for complications in proctectomy.  J Gastrointest Surg. 2014;18(5):986-994.PubMedGoogle ScholarCrossref
    39.
    Hyder  JA, Reznor  G, Wakeam  E, Nguyen  LL, Lipsitz  SR, Havens  JM.  Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP  [published online December 31, 2015].  Ann Surg. doi:10.1097/SLA.0000000000001558.PubMedGoogle Scholar
    40.
    Trotter  JF, Olson  J, Lefkowitz  J, Smith  AD, Arjal  R, Kenison  J.  Changes in international normalized ratio (INR) and Model for Endstage Liver Disease (MELD) based on selection of clinical laboratory.  Am J Transplant. 2007;7(6):1624-1628.PubMedGoogle ScholarCrossref
    41.
    Hall  MH, Esposito  RA, Pekmezaris  R,  et al.  Cardiac surgery nurse practitioner home visits prevent coronary artery bypass graft readmissions.  Ann Thorac Surg. 2014;97(5):1488-1493.PubMedGoogle ScholarCrossref
    42.
    Shafi  S, Aboutanos  MB, Agarwal  S  Jr,  et al; AAST Committee on Severity Assessment and Patient Outcomes.  Emergency general surgery: definition and estimated burden of disease.  J Trauma Acute Care Surg. 2013;74(4):1092-1097.PubMedGoogle ScholarCrossref
    43.
    Joynt  KE, Jha  AK.  Thirty-day readmissions: truth and consequences.  N Engl J Med. 2012;366(15):1366-1369.PubMedGoogle ScholarCrossref
    44.
    Kocher  RP, Adashi  EY.  Hospital readmissions and the Affordable Care Act: paying for coordinated quality care.  JAMA. 2011;306(16):1794-1795.PubMedGoogle ScholarCrossref
    45.
    Knaus  WA, Draper  EA, Wagner  DP, Zimmerman  JE.  APACHE II: a severity of disease classification system.  Crit Care Med. 1985;13(10):818-829.PubMedGoogle ScholarCrossref
    ×