SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

Background

Risk factors for emergent readmissions or death after acute myocardial infarction (AMI) are important in identifying patients at risk for major adverse events. However, there has been limited investigation conducted of prospective clinical registries to determine relevant risk factors.

Hypothesis

We hypothesize 30-day readmission or death could be predicted using patient, procedural, and process factors.

Methods

Patients presenting with ST-elevation myocardial infarction (STEMI) from 2006 to 2011 were prospectively enrolled in a STEMI registry (1271 patients). Thirty-day readmission was ascertained by administrative claims data. Death was determined by linking to the Social Security Death Master File. Univariate and stepwise multivariate logistic regression was conducted with Hosmer-Lemeshow goodness-of-fit statistics for model calibration and receiver operating characteristic (ROC) curve for model discrimination.

Results

The combined end point of 30-day readmission or postdischarge death included 135 patients (10.6%), including 109 emergent readmissions and 26 deaths. Factors associated with an increase risk of 30-day readmission or postdischarge death included age ≥80 years, diabetes, chest pain or cardiac arrest at presentation, and 3-vessel disease found at initial angiography. Factors associated with a decreased risk of 30-day readmission or postdischarge death included transfer to the catheterization lab from another emergency department, clopidogrel given during the procedure hypercholesterolemia, and receiving aspirin, β-blockers, and angiotensin-converting enzyme or angiotensin receptor blocker inhibitors at discharge. Index admission outcomes indicative of readmission or death postdischarge only included a new diagnosis of congestive heart failure. The model discriminated well with an ROC of 0.71 (95% confidence interval: 0.66-0.76).

Conclusions

Prehospitalization factors are overlooked and are important factors to incorporate in routine risk prediction models for readmission or death within 30 days following an AMI.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

Approximately 20% of Medicare beneficiaries are rehospitalized within 30 days of a hospital admission.[1] Beginning in fiscal year 2012, hospital readmissions following acute myocardial infarction (AMI) and other conditions will be subject to marked reductions in reimbursement from the Center for Medicaid and Medicare Services (section 3025 of the Patient Protection and Affordable Care Act).[2, 3] Current risk modeling for 30-day readmission following AMI has focused on patient risk factors derived from claims.[4] Other investigations in all angioplasty patients have reported limited success in discrimination and ability to predict 30-day readmissions alone.[5, 6] However, if patient safety is the goal, prediction models should focus on both readmission and the competing risk of mortality in the first 30 days after discharge. Currently, there has been little evidence developed to investigate readmission and the competing risk of death following ST-segment elevated myocardial infarction (STEMI),[7, 8] and limited evidence regarding risk factors for or the underlying principal causes of readmission following STEMI.[9, 10] Moreover, evidence has been sparse in describing these phenomena in rural tertiary care settings. Therefore, we sought to determine the underlying principal causes of readmission in a regional STEMI registry and develop a comprehensive predictive model for 30-day emergent readmission or postdischarge death.

Methods

Dartmouth-Hitchcock Medical Center is a rural tertiary care setting. Since 2001, all patients presenting initially to the Dartmouth-Hitchcock Medical Center emergency department, as well as all emergency transfers who have not received thrombolytic therapy, have been treated with a strategy of immediate primary percutaneous coronary intervention (PCI). All emergency transfers who have received thrombolytic therapy at the referring facility have been treated with a strategy of pharmacoinvasive PCI. All patients presenting to the Dartmouth-Hitchcock Medical Center cardiac catheterization laboratory with a diagnosis of STEMI between 2006 and 2011 were prospectively enrolled in the regional STEMI registry. Of the 1415 patients who were enrolled in the registry during the study period, 106 patients were excluded due to false STEMI diagnosis and 38 index admission deaths, leaving 1271 STEMI patients for analysis. The study was reviewed and approved by the Center for Protection of Human Subjects.

Preadmission emergency medical service, index hospital, and procedural data were collected by clinical staff and recorded in a prospective STEMI registry. Death following the index admission was obtained from the Social Security Death Master File. Hospital readmissions were defined by the time from STEMI admission to 30 days. Readmissions were obtained through Dartmouth-Hitchcock's electronic data warehouse. Once readmission was determined, 2 investigators adjudicated the principal reason for readmission (Table 1). All readmissions were included in the principal causes of readmission. Only emergent readmissions and postdischarge death occurring within 30 days of the STEMI admission were included for the development of the prediction model. Primary cause of death within 30 days was not available.

Table 1. Principal Causes of Readmission
 EventsPercent
Ischemic heart disease5443%
Chest pain2016%
Gastrointestinal108%
Heart failure97%
Arrhythmia43%
Atherosclerosis43%
Valve disease32%
Renal failure32%
Respiratory32%
Medication32%
Shock22%
Cerebral22%
Bleeding22%
Other86%

Statistical Methods

Patient demographics, comorbidities, procedural variables, in-hospital outcomes, and discharge location were evaluated for inclusion in the prediction model using χ2 and Student t test comparisons and univariate logistic regression.[11, 12] Using only factors with univariate associations with the composite end point of death or readmission, with an α of 0.10, both forward and backward step logistic regression were conducted to determine factors to be included in the multivariable model using an α of 0.10. Factors remaining in the multivariate models were evaluated, and only factors with an alpha of <0.10 were included in the final prediction model. First-order interaction terms were evaluated. Model calibration was assessed using plots of observed and expected by deciles of predicted risk using the methods by Lemeshow and Hosmer.[13] The discriminating ability of the final multivariate logistic regression model was assessed by estimating the area under the receiver operating characteristic (ROC) curve C-statistic (ROC area).[14] The bootstrapping method was used to calculate the 95% confidence interval (CI) around the ROC curve.[15]

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

There were 14 elective readmissions, 109 emergent readmissions, and 26 postdischarge deaths within 30 days of STEMI. The principal causes of any readmission are listed in Table 1, the most common of which included ischemic heart disease (43% of all readmissions), chest pain (20%), gastrointestinal complications (8%), and heart failure (7%). The combined end point of 30-day emergent readmission or death included 135 patients (11.7%), including 109 emergent readmissions and 26 deaths. Patient demographics and characteristics are reported in Table 2. Table 3 summarizes periprocedural characteristics, hospitalization risk factors, and discharge medications. Patient, procedural, and hospital factors included in the final multivariate model are reported in Table 4. Risk factors associated with 30-day emergent readmission or postdischarge death following acute hospitalization for STEMI included age ≥80 years, diabetes, chest pain at presentation, cardiac arrest at presentation, and 3-vessel disease. Preadmission transfer to the catheterization lab from another emergency department and hypercholesterolemia were factors that lowered the risk of readmission or postdischarge death. Clopidogrel given during the procedure was protective against readmission and postdischarge death. Index admission outcomes indicative of readmission or death only included a new diagnosis of congestive heart failure. The final multivariate model for readmission or death had good calibration with the Hosmer-Lemeshow χ2 of 8.0 and P = 0.4. The model discriminated well with an ROC of 0.71 and 95% CI: 0.66-0.76.

Table 2. Patient and Disease Characteristics
Patient FactorsReadmitted or Death Within 30 DaysNot Readmitted and Alive at 30 DaysP Valuea
  1. Abbreviations: BMI, body mass index; PCI, percutaneous coronary intervention; SD, standard deviation; TIMI, Thrombolysis in Myocardial Infarction. TIMI Flow Pre, TIMI flow measured prior to intervention; TIMI Flow Post, TIMI flow measured after intervention.

  2. a

    Pearson χ2 or Student t test and SD.

Alive discharges (N = 1271)135 (10.6%)1136 
Patient comorbidities   
    Age ± SD, y66.3 ± 14.862.3 ± 12.9<0.001
    Age ≥80 years, %23.010.6<0.001
    Female, %37.326.90.006
    Angina, %28.026.30.646
    Prior myocardial infarction, %29.823.60.082
    Prior coronary artery bypass surgery, %3.13.90.639
    Prior cerebrovascular accident, %11.25.90.011
    Diabetes mellitus, %28.020.90.042
    Hypertension, %63.459.00.288
    Underweight (BMI < 18.5), %1.91.60.772
    Obese (BMI >30), %36.733.40.411
    Aspirin therapy, %39.837.90.649
    Hypercholesterolemia, %51.451.40.114
    Statin therapy, %33.530.80.488
    Chest pain, %95.092.00.179
    Smoker, %31.736.00.280
Presentation   
    Transfer patient69.678.00.017
    Cardiac arrest, %16.86.2<0.001
    Shortness of breath, %54.742.40.003
    Syncope, %13.76.60.001
    Symptom duration ± SD, min267 ± 474252 ± 5770.752
    Anterior ST-elevation, %45.335.80.018
    Inferior ST-elevation, %49.160.00.008
    Lateral ST-elevation, %23.621.70.591
    Left bundle branch block, %3.12.80.820
    Anterior ST-depression, %28.029.00.791
    Sinus rhythm, %80.888.40.006
    Systolic blood pressure ± SD118 ± 40127 ± 390.005
    Diastolic blood pressure ± SD69 ± 2475 ± 240.009
    Heart rate ± SD74 ± 2673 ± 240.571
    Killip class IV, %12.43.3<0.001
    Primary PCI, %52.642.00.019
    Full dose lytic given, %21.729.40.044
    Half dose lytic given, %19.922.50.455
    Persistent pain, %50.949.40.712
    Persistent ST-elevations, %68.365.70.511
    Shock, %22.47.3<0.001
    Intubation, %19.96.1<0.001
    TIMI flow pre   
        047.843.00.096
        111.87.7 
        219.322.0 
        321.127.4 
Coronary disease   
    Left main disease >50%, %8.75.50.105
    2-Vessel disease >70%, %25.529.90.241
    3-vessel disease >70%, %38.523.1<0.001
    Infarcted left anterior descending, %42.933.00.013
    Infarcted left circumflex, %14.314.40.981
    Infarcted right coronary, %36.743.10.121
    Infarcted ramus, %0.60.60.941
    Infarcted left main, %1.90.60.070
    TIMI flow post   
        010.613.10.001
        11.90.2 
        23.71.4 
        383.985.4 
Table 3. Procedural Characteristics, Outcomes, and Discharge Medications
Procedural Factors and OutcomesReadmitted or Death Within 30 DaysNot Readmitted and Alive at 30 DaysP Valuea
  1. Abbreviations: ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; BB, β-blocker; MI, myocardial infarction; PCI, percutaneous coronary intervention; SD, standard deviation.

  2. a

    Pearson χ2 or Student t test and SD.

Procedure characteristics   
    Door-to-balloon time within 90 minutes, %14.912.70.437
    Cardiac catheterization within 4 hours, %88.889.40.817
    Attempted PCI, %90.187.80.401
    Stent, %85.184.70.907
    2b3a Inhibitor during catheterization, %46.046.10.973
    Antithrombotic during catheterization, %93.892.90.666
    Clopidogrel during catheterization, %54.765.50.007
    Heparin, %62.265.30.476
    Bivalirudin, %18.518.20.933
    Thrombectomy, %31.132.30.779
    Intra-aortic balloon pump, %19.97.6<0.001
    Intubation during catheterization, %10.63.2<0.001
In-hospital outcomes   
    Recurrent MI, %1.21.20.574
    Persistent neurological deficit %3.71.20.010
    Cerebrovascular accident, %5.01.2<0.001
    Intracranial hemorrhage, %1.20.40.162
    Congestive heart failure, %26.79.2<0.001
    Coronary artery bypass graft surgery, %3.72.10.177
    Left ventricular gram, %78.371.80.083
    Ejection fraction (%) ± SD46.0 ± 13.951.4 ± 11.3<0.001
    Ejection fraction <40%, %25.512.3<0.001
    Length of stay (days) ± SD5.7 ± 4.84.6 ± 3.1<0.001
    Length of stay >6 days, %28.015.8<0.001
Discharge medications   
    Aspirin, %87.997.5<0.001
    β-Blockers, %87.996.0<0.001
    ACE or ARB inhibitors, %74.080.70.078
    Aspirin, BB, and ACE or ARB combined, %64.474.60.012
    Calcium channel blockers, %6.77.10.859
    Lipid-lowering agents, %87.196.6<0.001
    Antiarrhythmics, %5.82.30.023
    Platelet inhibitors, %94.195.70.441
    Warfarin, %22.016.10.088
Table 4. Final Multivariate Logistic Model for Emergent 30-Day Readmission or Death
Risk FactorAdjusted Odds Ratio95% CIP Value
  1. Abbreviations: ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CI, confidence interval.

Patient comorbidities   
    Age ≥80 years2.251.39-3.630.001
    Diabetes1.901.21-2.960.005
    Hypercholesterolemia0.610.41-0.910.016
Presentation and disease   
    Transfer patient0.560.37-0.850.006
    Chest pain3.371.35-8.430.009
    Cardiac arrest3.021.71-5.34<0.001
    3-Vessel disease1.951.31-2.900.001
    Clopidogrel given during procedure0.690.47-1.020.061
In-hospital outcomes   
    In-hospital new heart failure2.151.32-3.510.002
Discharge medications   
    Aspirin, β-blocker, ACE or ARB inhibitors combined0.670.45-0.990.050

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

We have developed a comprehensive model for estimating risk of 30-day emergent readmission or postdischarge death after STEMI. Because this combined end point encompasses nearly all patients with major adverse events, the creation of this model is intended to assist in addressing the problem of readmission without ignoring patient safety. We report on patient factors in addition to STEMI presentation factors, and procedural and index hospitalization risk factors for readmission or postdischarge death within 30 days of STEMI with an ROC of 0.71 and 95% CI: 0.66-0.76. We are among the first to report on novel risk factors for readmission and death including presenting a history of chest pain, shortness of breath, cardiac arrest, and whether or not the patient was transferred from another facility. The incorporation of these factors into routine clinical risk models for 30-day readmission or death should be considered.

There is sparse evidence on the principal causes of readmission following acute hospitalization for STEMI. We demonstrated in Table 1 the principal causes of 30-day readmission, including elective (staged) and emergent readmissions. We found the most common causes of readmission were ischemic heart disease, chest pain, gastrointestinal complications, and heart failure (7%). We observed similar results to previous reports on PCI causes of readmissions listing ischemic heart disease, chest pain, and heart failure as 3 of the leading causes of readmission after PCI.[5, 10] However, we expanded our modeling to incorporate 30-day mortality in an effort to cluster 2 major accepted 30-day quality measures of patient safety, and move the field toward the development of predictive models targeting patient safety measures and not readmissions alone, which could be used to help institutions reduce readmissions but not at the expense of patient safety.

Recently, there has been an emergence of evidence from major state-based registries and hospitals reporting on the risk factors associated with 30-day readmission after AMI. However, these reports have predominantly focused their modeling efforts on the entire PCI population and were not restricted to PCI following STEMI.[5, 10] Various investigations that have developed models to predict 30-day readmission have been moderate in performance including reports using Medicare claims (ROC 0.63)[16] and state registries where all PCI procedures reported a readmission rate of 12.4% to 15.6%, however with poor discrimination with ROC curves ranging from 0.64 to 0.67.[5, 10] Others have reported the composite outcome of readmission or death within 30 days at 9.4% and 0.68%, respectively.[6] Four common risk scores have been developed to predict 30-day and 1-year mortality after STEMI, including the Controlled Abciximab and Device Investigation to Lower Late Angioplasty complications (CADILLAC),[17] Thrombolysis In Myocardial Infarction (TIMI),[18] Global Registry for Acute Coronary Events (GRACE),[19] and Primary Angioplasty in Myocardial Infarction (PAMI).[20] These risk scores have been externally validated and perform with moderate to good discrimination, with ROCs from 0.72 to 0.82.[21] Several shared risk factors in these models with our composite outcome include older age, 3-vessel disease, and diabetes. We expanded on the science by contributing risk factors derived from the time of presentation with STEMI, including whether or not the patient was transferred from another facility, chest pain, and cardiac arrest at the time of STEMI presentation. We also expanded the research in this field by combining 2 major patient safety quality measures, 30-day readmission and 30-day all-cause mortality after alive discharge, in an effort to redirect the field to routinely model early postdischarge adverse events that are threats to patient safety and not solely on readmission. Unfortunately, the majority of these preliminary reports on readmission do not separate out acute hospitalization for AMI or primary PCI.[5, 6, 10, 22] We contributed to the literature a clinical risk model for STEMI 30-day readmission or death and evaluate risk factors at the time of presentation with STEMI.

There are several limitations of this investigation to consider. First, we presented results from a single rural academic tertiary medical center receiving STEMI patients from Vermont and New Hampshire. Nearly all of our patients were treated with an initial strategy of either primary PCI or pharmacoinvasive PCI, and therefore our report may not be generalizable to medical centers performing predominantly primary PCI for STEMI or those treating predominantly with a stand-alone lytic therapy strategy. Regardless, our results appear to be consistent with the PCI models reported from other regions including New York, Massachusetts, and Minnesota. Moreover, we used prospectively collected STEMI registry data to measure, for the first time, STEMI presentation and prehospitalization measures with readmission. Although it is possible that unmeasured factors during the index admission or postdischarge may be associated with readmission (eg, discharge planning and quality, medications, medication compliance, and referrals to primary care and cardiac rehabilitation services), we believe that along with the inclusion of STEMI processes of care, using patient and procedural factors is a comprehensive approach toward developing a prediction model for STEMI readmission, and contributes novel evidence on modeling readmission and mortality. In our modeling, we observed transfer patients to be a protective factor against 30-day death or readmission. There are several reasons our transfer patients may be a protective population. We limited our analysis to patients alive at discharge. Patients transferred to our hospital from an outlying hospital must have survived both the transit and index admission to be included in the analysis. It is likely that high-risk patients or patients in shock died prior to arrival during a transfer or did not survive the index admission, leaving only low- to moderate-risk patients alive at discharge among the transfer patients. Over the study period we also noted a marked improvement in transfer 30-day mortality and reduction in door-to-needle and door-to-balloon times. Finally, the ascertainment of readmission was conducted using hospital administration data. In our region, all major cardiac complications are referred back to our tertiary medical center for observation, readmission, or repeat angiography with or without angioplasty. As noted by Desai and colleagues,[9] conducting readmission investigations in the United States requires the use of Medicare data to completely ascertain the end point of readmission. Although we did not link our data to Medicare claims, in doing so, the ascertainment of readmission would only supplement patients over the age of 65 years and not include HMO-covered beneficiaries under Medicare (in some geographic regions, the HMO penetration for Medicare beneficiaries is more than 50%), and will still underestimate the ascertainment of readmission for the uninsured, those under 65 years, and those over 65 years HMO-covered Medicare beneficiaries.[9]

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

We present a prediction model for 30-day emergent readmission or postdischarge death following acute hospitalization for STEMI. We report on novel patient and modifiable risk factors derived from the time of presentation with STEMI and their utility in predicting readmission or death. Our model can be used to develop routine surveillance systems when planning for STEMI discharge to determine the likelihood of near-term adverse events that may eminently jeopardize patient safety.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References

The authors acknowledge Sheila Conley and the Dartmouth-Hitchcock Medical Center STEMI Registry team members and staff for contributing patient, procedural, and outcomes data to the registry.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. References
  • 1
    Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360:14181428.
  • 2
    The Patient Protection and Affordable Care Act. 42 USC §3025 (2010). http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed May 29, 2013.
  • 3
    Angelelli J, Gifford D, Intrator O, et al. Access to postacute nursing home care before and after the BBA. Balanced Budget Act. Health Aff (Millwood). 2002;21:254264.
  • 4
    Keenan PS, Normand SL, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:2937.
  • 5
    Hannan EL, Zhong Y, Krumholz H, et al. 30-day readmission for patients undergoing percutaneous coronary interventions in New York state. JACC Cardiovasc Interv. 2011;4:13351342.
  • 6
    Khawaja FJ, Shah ND, Lennon RJ, et al. Factors associated with 30-day readmission rates after percutaneous coronary intervention. Arch Int Med. 2012;172:112117.
  • 7
    Lee JH, Yang DH, Jang SY, et al. Incremental predictive value of red cell distribution width for 12-month clinical outcome after acute myocardial infarction [published online ahead of print April 8, 2013]. Clin Cardiol. doi: 10.1002/clc.22114.
  • 8
    Ephrem G. Red blood cell distribution width is a predictor of readmission in cardiac patients. Clin Cardiol. 2013;36:293299.
  • 9
    Desai MM, Stauffer BD, Feringa HH, et al. Statistical models and patient predictors of readmission for acute myocardial infarction: a systematic review. Circ Cardiovasc Qual Outcomes. 2009;2:500507.
  • 10
    Yeh RW, Rosenfield K, Zelevinsky K, et al. Sources of hospital variation in short-term readmission rates after percutaneous coronary intervention. Circ Cardiovasc Interv. 2012;5:227236.
  • 11
    Hennekens CH, Buring JE. Epidemiology in Medicine. Boston, MA: Little Brown & Co.; 1988.
  • 12
    Armitage P, Berry G. Statistical Methods in Medical Research. London, UK: Blackwell Scientific Publications; 1987.
  • 13
    Lemeshow S, Hosmer DW. The use of goodness-of-fit statistics in the development of logistic regression models. Am J Epidemiol. 1982;115:92106.
  • 14
    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:2936.
  • 15
    Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci. 1986;1:5477.
  • 16
    Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4:243252.
  • 17
    Halkin A, Singh M, Nikolsky E, et al. Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score. J Am Coll Cardiol. 2005;45:13971405.
  • 18
    Morrow DA, Antman EM, Charlesworth A, et al. TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation. 2000;102:20312037.
  • 19
    Yan AT, Yan RT, Tan M, et al. In-hospital revascularization and one-year outcome of acute coronary syndrome patients stratified by the grace risk score. Am J Cardiol. 2005;96:913916.
  • 20
    Addala S, Grines CL, Dixon SR, et al. Predicting mortality in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention (PAMI risk score). Am J Cardiol. 2004;93:629632.
  • 21
    Lev EI, Kornowski R, Vaknin-Assa H, et al. Comparison of the predictive value of four different risk scores for outcomes of patients with ST-elevation acute myocardial infarction undergoing primary percutaneous coronary intervention. Am J Cardiol. 2008;102:611.
  • 22
    Przybysz-Zdunek B, Ploch M, Pluta W, et al. All-cause readmission and repeat revascularization after percutaneous coronary intervention. Cardiol J. 2012;19:174179