0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

A Prediction Rule to Identify Low-Risk Patients With Pulmonary Embolism FREE

Drahomir Aujesky, MD, MSc; D. Scott Obrosky, MSc; Roslyn A. Stone, PhD; Thomas E. Auble, PhD; Arnaud Perrier, MD; Jacques Cornuz, MD, MPH; Pierre-Marie Roy, MD, PhD; Michael J. Fine, MD, MSc
[+] Author Affiliations

Author Affiliations: Division of General Internal Medicine, Department of Medicine (Drs Aujesky and Fine), Department of Biostatistics, Graduate School of Public Health (Dr Stone), and Department of Emergency Medicine (Dr Auble), University of Pittsburgh, and VA Center for Health Equity Research and Promotion and VA Pittsburgh Healthcare System (Drs Stone and Fine and Mr Obrosky), Pittsburgh, Pa; Department of Internal Medicine, University Outpatient Clinic, and Clinical Epidemiology Center, University of Lausanne, Lausanne (Drs Aujesky and Cornuz), and Division of General Internal Medicine, Department of Internal Medicine, University of Geneva, Geneva (Dr Perrier), Switzerland; and Department of Emergency Medicine, University of Angers, Angers, France (Dr Roy).


Arch Intern Med. 2006;166(2):169-175. doi:10.1001/archinte.166.2.169.
Text Size: A A A
Published online

Background  A simple prognostic model could help identify patients with pulmonary embolism who are at low risk of death and are candidates for outpatient treatment.

Methods  We randomly allocated 15 531 retrospectively identified inpatients who had a discharge diagnosis of pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our rule to predict 30-day mortality using classification tree analysis and patient data routinely available at initial examination as potential predictor variables. We used data from a European prospective study to externally validate the rule among 221 inpatients with pulmonary embolism. We determined mortality and nonfatal adverse medical outcomes across derivation and validation samples.

Results  Our final model consisted of 10 patient factors (age ≥70 years; history of cancer, heart failure, chronic lung disease, chronic renal disease, and cerebrovascular disease; and clinical variables of pulse rate ≥110 beats/min, systolic blood pressure <100 mm Hg, altered mental status, and arterial oxygen saturation <90%). Patients with none of these factors were defined as low risk. The 30-day mortality rates for low-risk patients were 0.6%, 1.5%, and 0% in the derivation, internal validation, and external validation samples, respectively. The rates of nonfatal adverse medical outcomes were less than 1% among low-risk patients across all study samples.

Conclusions  This simple prediction rule accurately identifies patients with pulmonary embolism who are at low risk of short-term mortality and other adverse medical outcomes. Prospective validation of this rule is important before its implementation as a decision aid for outpatient treatment.

Figures in this Article

Pulmonary embolism (PE) is a major health problem, with an estimated incidence of 23 to 69 cases per 100 000 persons annually in the United States.1,2 Data from the National Hospital Discharge Survey3 show that 101 000 patients were hospitalized in 2002 in acute care hospitals having a primary diagnosis of PE in the United States, resulting in 676 700 inpatient days. The all-cause short-term mortality of this illness varies widely, ranging from more than 95% among patients who experience cardiorespiratory arrest to less than 2% among patients with nonmassive PE,46 defined as PE without systemic hypotension, cardiogenic shock, or respiratory failure.7

There is growing evidence that outpatient treatment with low-molecular-weight heparin sodium is effective and safe for selected patients with nonmassive PE.810 Based on this evidence, experts11,12 and the British Thoracic Society guidelines for the management of acute PE13 recommend outpatient treatment for clinically stable patients. Outpatient treatment for nonmassive PE is not widely accepted because no explicit clinical criteria exist to accurately identify patients with PE who are at low risk of adverse outcomes. Therefore, we sought to develop an objective and easily applied clinical prediction rule to identify patients with PE at low risk of short-term mortality and other adverse medical outcomes who are candidates for outpatient treatment.

PATIENT IDENTIFICATION AND ELIGIBILITY

We identified patients with PE using the Pennsylvania Health Care Cost Containment Council database,14 which contains information on demographics, source of admission, admission and discharge dates, inpatient mortality data, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis and procedure codes for patients admitted to all nongovernmental acute care hospitals in Pennsylvania. Our study included inpatients 18 years or older who had a primary discharge diagnosis of acute PE between January 1, 2000, and November 30, 2002, based on the following ICD-9-CM codes: 415.1, 415.11, 415.19, and 673.20 through 673.24. To ensure that we identified the most severely ill patients with PE as the primary reason for hospitalization, we also included inpatients with a secondary ICD-9-CM code for PE and 1 of the following primary codes that may represent complications or treatments of PE: respiratory failure (ICD-9-CM code 518.81), cardiogenic shock (ICD-9-CM code 785.51), cardiac arrest (ICD-9-CM code 427.5), secondary pulmonary hypertension (ICD-9-CM code 416.8), syncope (ICD-9-CM code 780.2), thrombolysis (ICD-9-CM code 99.10), and intubation or mechanical ventilation (ICD-9-CM codes 96.04, 96.05, and 96.70-96.72). Because patients with recurrent PE may have a higher mortality than patients with a single episode,15,16 we included all episodes of PE for the same patient within the study period to avoid potential selection bias. We did not include patients who had only a secondary ICD-9-CM code for PE or who were transferred from another health care facility, because such patients are more likely to have PE as a complication of hospitalization (eg, after surgery). Because outpatient treatment for PE was not considered usual care between 2000 and 2002, it is likely that we captured most patients having a primary diagnosis of PE in Pennsylvania during this period. This study was approved by the institutional review board of the University of Pittsburgh, Pittsburgh, Pa.

BASELINE PREDICTOR VARIABLES

The baseline clinical variables necessary to derive our prediction rule were obtained from the Atlas database (MediQual, Marlborough, Mass).14 Clinical inpatient data from all nongovernmental acute care hospitals in Pennsylvania are represented in this proprietary database, which is compiled from patient medical records using standardized data collection instruments.

We used vital signs measured in the emergency department for all patients admitted through the emergency department; all other variables were recorded on the day of hospital admission. For patients admitted from other sources (eg, directly from a physician’s office), we abstracted all clinical variables on the day of admission. To derive our prediction rule, we used clinical variables routinely available to clinicians at the time of initial examination and previously shown to be associated with short-term mortality in patients who have PE or other acute diseases (Table 1). We did not consider other potential predictors such as right ventricular dysfunction, mean pulmonary arterial pressure, or concomitant deep vein thrombosis shown by sonography because these conditions are not routinely assessed among patients diagnosed as having PE.17,20,31

Table Graphic Jump LocationTable 1. Predictor Variables Considered
OUTCOME MEASURES

The main study outcome used to derive our prediction rule was death from all causes within 30 days of each hospitalization. All-cause 30-day mortality is objective and clinically relevant and is a widely used outcome of prognostic models for other acute diseases or medical interventions.3234 Most deaths due to PE occur within this time frame.35 We obtained mortality data from the National Death Index.36 Using Atlas database information and discharge ICD-9-CM codes from the Pennsylvania Health Care Cost Containment Council database, we also assessed whether patients classified as low risk by our prediction rule developed nonfatal cardiogenic shock (ICD-9-CM code 785.51) or cardiorespiratory arrest, defined as cardiac arrest (ICD-9-CM code 427.5), resuscitation (ICD-9-CM codes 99.60, 99.63, and 37.91), intubation (ICD-9-CM codes 96.04 and 96.05), or mechanical ventilation (ICD-9-CM codes 96.70-90.72).

DERIVATION, INTERNAL VALIDATION, AND EXTERNAL VALIDATION OF THE PREDICTION RULE

Of the 16 468 patient discharges that met our inclusion criteria, we excluded 937 because they were missing patient identifiers (n = 867) or could not be linked to the National Death Index (n = 70). Therefore, the study cohort comprised 15 531 patients who had a discharge diagnosis of PE from 186 Pennsylvania hospitals. Overall, these discharges represented 14 672 individual patients with PE; 859 discharges (6%) represented recurrent PE episodes that occurred during the study period. We randomly selected 10 354 discharges (67%) for the derivation sample and 5177 discharges (33%) for the internal validation sample.

We derived our prediction rule using classification tree analysis,37 with 30-day mortality as the outcome and the demographic and clinical variables in Table 1 as predictors. Except for age, we dichotomized continuous variables using clinically meaningful cutoff points that are commonly used in clinical practice and are easily remembered by physicians (eg, systolic blood pressure <100 mm Hg and arterial oxygen saturation <90%). Unknown values were assumed to be normal, a strategy successfully used in the derivation and validation of a widely used previous prognostic model for pneumonia.32 Using S-Plus 2000 software,38 we recursively partitioned our derivation sample into progressively more homogeneous subgroups by sequentially identifying predictor variables that best discriminated between patients who died and those who did not. At each step, the program automatically examined all possible splits for age and each categorical predictor to identify the variable and cutoff point that maximized goodness of fit. The splitting process continued until the subgroups were homogeneous or contained fewer than 3 deaths. Although we did not modify the automatically generated models using subjective criteria, we rounded cutoff points to the next clinically meaningful value. We explored candidate tree models with and without laboratory variables, trying to find models that identified a low-risk group with a membership of at least 20% of the total derivation sample and a 30-day mortality of less than 1%. Although no widely accepted threshold defines low risk, prognostic models for other acute diseases such as community-acquired pneumonia or heart failure defined short-term mortality rates below 1% to 2% as low risk.14,32 Among candidate models meeting these criteria, we chose the one with the fewest predictors.

We then assessed the performance of our prediction rule in the internal validation sample by computing the proportion of patients who were classified as low vs higher risk and the proportion of patients who died within 30 days of initial examination. Because 7-day mortality may be more relevant for the hospital admission decision than 30-day mortality, we also estimated the proportion of patients in both samples who died 7 days after admission or experienced nonfatal cardiogenic shock or cardiorespiratory arrest in the hospital. We externally validated our rule using data previously collected from a prospective cohort study39 that used spiral computed tomography to diagnose PE. That study enrolled patients with suspected PE from 3 emergency departments at the university hospitals of Lausanne, Geneva, and Angers between October 1, 2000, and June 30, 2002. Patients who had a contraindication to spiral computed tomography (allergy to iodine contrast agents, creatinine clearance <30 mL/min, or pregnancy), severely ill patients (massive PE with shock or expected survival <3 months), or those unable to provide signed informed consent because of cognitive impairment were excluded from that study. Baseline patient characteristics, including the predictors that comprise our rule, were collected in the emergency department. The criteria used to establish the diagnosis of PE are described elsewhere.39 Death, objectively confirmed recurrent venous thromboembolism,major bleeding (defined as retroperitoneal, joint, or cerebral bleeding or any bleeding requiring transfusion), and the timing of these adverse events were documented during a 3-month follow-up. Of 1290 screened patients with suspected PE, 965 (75%) were eligible for the study.39 Eligible patients were younger and had fewer comorbid conditions than excluded patients. For our external validation, we used data from 221 of 222 patients with objectively confirmed PE enrolled in that study,39 excluding 1 patient who was lost to follow-up. We then estimated the proportion of patients classified as low risk by our prediction rule and the proportion of patients who died 7 days, 30 days, and 90 days after the initial examination. We also assessed whether patients developed nonfatal recurrent venous thromboembolism or had major bleeding during follow-up.

STATISTICAL ANALYSIS

We compared the mortality rates, proportions of patients classified as low risk vs higher risk, and nonfatal adverse medical outcomes among the derivation and internal and external validation samples using logistic regression analysis with a robust variance estimator to account for the clustering of patients who were discharged more than once for PE during the study period. For comparisons involving observed zeros, we used exact χ2 tests. A 2-sided P<.05 was considered statistically significant. To assess the accuracy of our rule to predict 30-day mortality, we also compared sensitivity, specificity, positive and negative predictive values, and likelihood ratios for low-risk vs higher-risk patients across derivation and validation samples.

BASELINE PATIENT CHARACTERISTICS

Relative to the derivation and internal validation samples, patients in the external validation sample had a lower prevalence of most comorbid illnesses and fewer abnormal findings on physical examination, reflecting the exclusion of severely ill patients from the study39 used to externally validate the rule (Table 2). Thirty-day mortality in the derivation, internal validation, and external validation samples was 9.2%, 9.5%, and 2.7%, respectively.

Table Graphic Jump LocationTable 2. Baseline Patient Characteristics in the Derivation and Validation Samples*
DERIVATION OF THE PREDICTION RULE

Among the candidate tree models, we selected a model consisting of the following 10 easily ascertained and clinically relevant patient factors: age 70 years or older, a history of 5 comorbid diseases (cancer, heart failure, chronic lung disease, chronic renal disease, and cerebrovascular disease), and the presence of 4 clinical abnormalities (pulse rate ≥110 beats/min, systolic blood pressure <100 mm Hg, altered mental status, and arterial oxygen saturation <90%) (Figure). Patients with none of these factors were defined as low risk.This model, which does not rely on any laboratory variables, identified 21.6% (95% confidence interval [CI], 20.8%-22.4%) of patients in the derivation sample as low risk, with a corresponding 30-day mortality of 0.6% (95% CI, 0.3%-1.0%). Alternative tree models that included laboratory findings did not measurably increase the proportion of patients classified as low risk or decrease the mortality rate among low-risk patients compared with this model based on history and clinical variables alone.

Place holder to copy figure label and caption
Figure.

The clinical prediction rule. Cerebrovascular disease includes transient ischemic attack or stroke. Altered mental status includes disorientation, lethargy, stupor, or coma.

Graphic Jump Location

In the derivation sample, the 859 patients (6%) who developed recurrent PE during the study period had a slightly lower 30-day mortality (8.3%) than patients with a single PE episode (9.2%). The proportion of patients defined as low risk and the mortality of this subgroup did not change when we excluded the patients who had a discharge diagnosis of recurrent PE during the study period, nor when we excluded the 2% of patients who had primary ICD-9-CM codes for PE complications or treatments. This latter subgroup included the most severely ill patients in our study (mean 30-day mortality, 66%).

VALIDATION OF THE PREDICTION RULE

In the internal validation sample, our derived model classified 21.6% (95% CI, 20.5%-22.7%) of patients as low risk, with low-risk patients having a 30-day mortality of 1.5% (95% CI, 0.9%-2.4%) (Table 3). Although 30-day mortality was somewhat higher in the internal validation sample than in the derivation sample (P = .01), the 0.9% difference was small in absolute terms. Seven-day mortality was 0.4% (95% CI, 0.2%-0.7%) in the derivation sample and 0.9% (95% CI, 0.4%-1.6%) in the internal validation sample (P = .05). The rate of nonfatal cardiogenic shock or cardiorespiratory arrest among low-risk inpatients was 0.7% (95% CI, 0.4%-1.2%) in the derivation sample and 0.9% (95% CI, 0.4%-1.6%) in the internal validation sample (P = .58).

Table Graphic Jump LocationTable 3. Risk Classification and Outcomes for Patients in the Derivation and Validation Samples*

In the external validation sample, our prediction rule classified 33.9% (95% CI, 27.7%-40.6%) of patients as low risk, a higher proportion than in the derivation sample (P<.001) (Table 3). None of the low-risk patients in the external validation group died within 7 days or 30 days of the initial examination (P>.99 for both groups compared with the derivation sample). During the 3-month follow-up period, none of the low-risk patients in the external validation sample died, had recurrent venous thromboembolism, or experienced a major bleeding episode.

The rule had a high sensitivity (range, 97%-100%) and a high negative predictive value (range, 98%-100%) for predicting 30-day mortality (Table 4). Because the prediction rule was specifically designed to identify low-risk patients (ie, to rule out short-term mortality), the specificity (range, 23%-35%) and positive predictive value (range, 4%-12%) were low.

Table Graphic Jump LocationTable 4. Accuracy of the Prediction Rule to Predict 30-Day Mortality in the Derivation and Validation Samples*

We developed a simple clinical prediction rule based on 10 demographic, history, and clinical findings to identify low-risk patients with PE. Among large derivation and internal validation samples of patients with PE, our rule identified more than one fifth of patients at low risk of short-term mortality and serious medical complications. In an independent validation cohort of patients with PE, we confirmed the accuracy of our prediction rule: none of the patients classified as low risk died, experienced recurrent venous thromboembolism, or had major bleeding during a 3-month follow-up. Overall, our rule had a negative predictive value for 30-day mortality of at least 98% across the derivation and 2 validation samples.

The potential clinical and economic benefit of our prediction rule can be estimated using data from a recent cost-effectiveness analysis comparing inpatient treatment with unfractionated heparin vs low-molecular-weight heparin in patients with PE.40 Treatment with low-molecular-weight heparin was cost saving when at least 5% of patients were treated as outpatients or 8% were discharged early. Assuming a cost difference of $4500 between inpatient and outpatient treatment of PE40 and an annual PE incidence of 101 000 cases,3 up to $91 million per year could be saved in the United States if 20% of patients were treated as outpatients. Therefore, treating patients with PE identified as low risk using our prediction rule in an ambulatory setting could result in important cost savings. However, at the initial site of treatment decision for patients with PE, it is important for physicians also to consider psychosocial contraindications to outpatient care (eg, lack of treatment adherence). Other potential barriers to outpatient treatment are the lack of outpatient systems of health care and the absence of insurance coverage for more costly low-molecular-weight heparin.

Our prediction rule consists of 10 clinical prognostic factors that are routinely available in all hospital settings and that were previously shown to be associated with adverse outcomes among patients with PE and other acute diseases.1620,22 Compared with a previous prognostic model for PE,20,41 our prediction rule has distinctive strengths. First, our rule consists of clearly defined, routinely available predictors and does not require any laboratory tests or radiographic procedures not routinely performed in the management of PE. Second, the accuracy and generalizability of the rule are supported by its derivation and internal and external validation in 15 752 patients from 189 hospitals and 3 countries. Third, our study samples represent a broad disease spectrum, ranging from nonmassive PE to PE with cardiorespiratory arrest.

Investigators in a prior study9 successfully treated 81 (51%) of 158 patients with PE as outpatients using low-molecular-weight heparin. Patients without arterial hypotension, arterial hypoxemia, pain requiring intravenous narcotics, social contraindications to outpatient treatment, and comorbid conditions necessitating hospital treatment were eligible for that study, although the comorbid conditions requiring hospitalization were not specified. Moreover, patients enrolled in that study were younger and potentially healthier than the patients in our study samples, which may have resulted in a higher proportion of patients considered as low risk. In contrast to the unspecific eligibility criteria of the prior study,9 our prediction rule provides clinicians a set of explicit criteria to identify low-risk patients with PE.

However, patients classified as higher risk by our prediction rule (eg, patients with cancer) may choose to be treated as outpatients even if their short-term prognosis is worse than that in low-risk patients. Until randomized trials comparing inpatient vs outpatient treatment of PE are conducted, it remains uncertain whether the initial site of treatment affects mortality rates.

Our work has potential limitations. First, patients in our derivation and internal validation samples were identified using ICD-9-CM codes for PE rather than standardized clinical criteria and may be subject to study selection biases because of hospital coding procedures. However, prior studies4244 demonstrated that 94% to 96% of patients with specific ICD-9-CM codes for PE had objectively documented disease based on medical record review criteria. Second, we cannot exclude the possibility that patients who were identified using a primary ICD-9-CM code for conditions that may represent complications of PE (eg, cardiogenic shock) developed PE as a consequence of these conditions. However, the performance of our prediction rule did not change when these patients were excluded from analysis. Third, we had no information about the timeliness or type of treatments received (eg, type of heparin) among our study samples. Therefore, we could not assess whether patients who were treated differently experienced different outcomes. Fourth, we externally validated our rule using data from a prior prospective study39 that was not designed to validate our prediction rule. The small sample size in that study resulted in wide 95% CIs for mortality rates. Although the healthier patients in the external validation sample may not reflect the full prognostic spectrum of patients with PE, no deaths occurred in the subgroup that was identified as low risk by our prediction rule.

We derived and validated a clinical prediction rule that accurately identifies a substantial proportion of patients with PE who are at low risk of death and other adverse outcomes and who are candidates for less costly outpatient treatment. However, before this prediction rule can be considered ready for use in clinical practice, it should be validated in a prospective study.

Correspondence: Drahomir Aujesky, MD, MSc, Service de Médecine Interne, Centre Hospitalier Universitaire Vaudois, BH 10-622, 1011 Lausanne, Switzerland (aujesky@swissonline.ch).

Accepted for Publication: June 21, 2005.

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

Financial Disclosure: None.

Funding/Support: This study was funded by grant 1 R21 HL075521-01A1 from the National Heart, Lung, and Blood Institute, Bethesda, Md; by the Swiss Foundation in Medicine and Biology, Bern, and the Swiss Medical Association, Bern (Dr Aujesky); and by a K24 Career Development Award from the National Institute of Allergy and Infectious Diseases, Bethesda (Dr Fine).

Acknowledgment: We thank Kenneth J. Smith, MD, for his careful review of the manuscript and his comments on the analysis.

Anderson  FA  JrWheeler  HBGoldberg  RJ  et al.  A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism: the Worcester DVT Study. Arch Intern Med 1991;151933- 938
PubMed Link to Article
Silverstein  MDHeit  JAMohr  DNPetterson  TMO’Fallon  WMMelton  LJ  III Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study. Arch Intern Med 1998;158585- 593
PubMed Link to Article
Kozak  LJOwings  MFHall  MJ National Hospital Discharge Survey: 2002 annual summary with detailed diagnosis and procedure data. Vital Health Stat 13 2005;(158)1- 199
PubMed
Kurkciyan  IMeron  GSterz  F  et al.  Pulmonary embolism as a cause of cardiac arrest: presentation and outcome. Arch Intern Med 2000;1601529- 1535
PubMed Link to Article
Simonneau  GSors  HCharbonnier  B  et al. Thésée Study Group, A comparison of low-molecular-weight heparin with unfractionated heparin for acute pulmonary embolism. N Engl J Med 1997;337663- 669
PubMed Link to Article
Buller  HRDavidson  BLDecousus  H  et al. Matisse Investigators, Subcutaneous fondaparinux versus intravenous unfractionated heparin in the initial treatment of pulmonary embolism. N Engl J Med 2003;3491695- 1702[published correction appears in N Engl J Med. 2004;350:423]
PubMed Link to Article
Tapson  VFWitty  LA Massive pulmonary embolism: diagnostic and therapeutic strategies. Clin Chest Med 1995;16329- 340
PubMed
Wells  PSKovacs  MJBormanis  J  et al.  Expanding eligibility for outpatient treatment of deep venous thrombosis and pulmonary embolism with low-molecular-weight heparin: a comparison of patient self-injection with homecare injection. Arch Intern Med 1998;1581809- 1812
PubMed Link to Article
Kovacs  MJAnderson  DMorrow  BGray  LTouchie  DWells  PS Outpatient treatment of pulmonary embolism with dalteparin. Thromb Haemost 2000;83209- 211
PubMed
Wells  PSAnderson  DRRodger  MA  et al.  A randomized trial comparing 2 low-molecular-weight heparins for the outpatient treatment of deep vein thrombosis and pulmonary embolism. Arch Intern Med 2005;165733- 738
PubMed Link to Article
Wells  PS Outpatient treatment of patients with deep-vein thrombosis or pulmonary embolism. Curr Opin Pulm Med 2001;7360- 364
PubMed Link to Article
Janata  K Managing pulmonary embolism. BMJ 2003;3261341- 1342
PubMed Link to Article
British Thoracic Society Standards of Care Committee; Pulmonary Embolism Guideline Development Group, British Thoracic Society guidelines for the management of suspected acute pulmonary embolism. Thorax 2003;58470- 483
PubMed Link to Article
Auble  TEHsieh  MGardner  W  et al.  A prediction rule to identify low-risk patients with heart failure. Acad Emerg Med 2005;12514- 521
PubMed Link to Article
Douketis  JDKearon  CBates  SDuku  EKGinsberg  JS Risk of fatal pulmonary embolism in patients with treated venous thromboembolism. JAMA 1998;279458- 462
PubMed Link to Article
Goldhaber  SZVisani  LDe Rosa  M Acute pulmonary embolism: clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 1999;3531386- 1389
PubMed Link to Article
Carson  JLKelley  MADuff  A  et al.  The clinical course of pulmonary embolism. N Engl J Med 1992;3261240- 1245
PubMed Link to Article
Heit  JASilverstein  MDMohr  DNPetterson  TMO’Fallon  WMMelton  LJ  III Predictors of survival after deep vein thrombosis and pulmonary embolism: a population-based, cohort study. Arch Intern Med 1999;159445- 453
PubMed Link to Article
Grifoni  SOlivotto  ICecchini  P  et al.  Short-term clinical outcome of patients with acute pulmonary embolism, normal blood pressure, and echocardiographic right ventricular dysfunction. Circulation 2000;1012817- 2822
PubMed Link to Article
Wicki  JPerrier  APerneger  TVBounameaux  HJunod  AF Predicting adverse outcome in patients with acute pulmonary embolism: a risk score. Thromb Haemost 2000;84548- 552
PubMed
Giannitsis  EMuller-Bardorff  MKurowski  V  et al.  Independent prognostic value of cardiac troponin T in patients with confirmed pulmonary embolism. Circulation 2000;102211- 217
PubMed Link to Article
Kanich  WBrady  WJHuff  JS  et al.  Altered mental status: evaluation and etiology in the ED. Am J Emerg Med 2002;20613- 617
PubMed Link to Article
Peres Bota  DLopes Ferreira  FMelot  CVincent  JL Body temperature alterations in the critically ill. Intensive Care Med 2004;30811- 816
PubMed Link to Article
Umpierrez  GEIsaacs  SDBazargan  NYou  XThaler  LMKitabchi  AE Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab 2002;87978- 982
PubMed Link to Article
Hodkinson  HM Value of admission profile tests for prognosis in elderly patients. J Am Geriatr Soc 1981;29206- 210
PubMed
Herrmann  FRSafran  CLevkoff  SEMinaker  KL Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med 1992;152125- 130
PubMed Link to Article
Lee  CTGuo  HRChen  JB Hyponatremia in the emergency department. Am J Emerg Med 2000;18264- 268
PubMed Link to Article
Terzian  CFrye  EBPiotrowski  ZH Admission hyponatremia in the elderly: factors influencing prognosis. J Gen Intern Med 1994;989- 91
PubMed Link to Article
Steen  PMBrewster  ACBradbury  RCEstabrook  EYoung  JA Predicted probabilities of hospital death as a measure of admission severity of illness. Inquiry 1993;30128- 141
PubMed
Konstantinides  SGeibel  AOlschewski  M  et al.  Importance of cardiac troponins I and T in risk stratification of patients with acute pulmonary embolism. Circulation 2002;1061263- 1268
PubMed Link to Article
ten Wolde  MSohne  MQuak  EMac Gillavry  MRBuller  HR Prognostic value of echocardiographically assessed right ventricular dysfunction in patients with pulmonary embolism. Arch Intern Med 2004;1641685- 1689
PubMed Link to Article
Fine  MJAuble  TEYealy  DM  et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336243- 250
PubMed Link to Article
Mehr  DRBinder  EFKruse  RL  et al.  Predicting mortality in nursing home residents with lower respiratory tract infection: the Missouri LRI Study. JAMA 2001;2862427- 2436
PubMed Link to Article
van Gorp  MJSteyerberg  EWKallewaard  Mvan der Graaf  Y Clinical prediction rule for 30-day mortality in Bjork-Shiley convexo-concave valve replacement. J Clin Epidemiol 2003;561006- 1012
PubMed Link to Article
Douketis  JD Prognosis in pulmonary embolism. Curr Opin Pulm Med 2001;7354- 359
PubMed Link to Article
MacMahon  B The National Death Index. Am J Public Health 1983;731247- 1248
PubMed Link to Article
Breiman  LFriedman  JOlshen  R  et al.  Classification and Regression Trees.  Pacific Grove, Calif Wadsworth1984;
 S-Plus 2000 User’s Guide Data Analysis Products Division.  Seattle, Wash MathSoft2000;
Perrier  ARoy  PMAujesky  D  et al.  Diagnosing pulmonary embolism in outpatients with clinical assessment, D-dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study. Am J Med 2004;116291- 299
PubMed Link to Article
Aujesky  DSmith  KJCornuz  JRoberts  MS Cost-effectiveness of low-molecular-weight heparin for treatment of pulmonary embolism. Chest 2005;1281601- 1610
PubMed Link to Article
Nendaz  MRBandelier  PAujesky  D  et al.  Validation of a risk score identifying patients with acute pulmonary embolism, who are at low risk of clinical adverse outcome. Thromb Haemost 2004;911232- 1236
PubMed
White  RHGettner  SNewman  JMTrauner  KBRomano  PS Predictors of rehospitalization for symptomatic venous thromboembolism after total hip arthroplasty. N Engl J Med 2000;3431758- 1764
PubMed Link to Article
White  RHRomano  PSZhou  HRodrigo  JBargar  W Incidence and time course of thromboembolic outcomes following total hip or knee arthroplasty. Arch Intern Med 1998;1581525- 1531
PubMed Link to Article
Murin  SRomano  PSWhite  RH Comparison of outcomes after hospitalization for deep venous thrombosis or pulmonary embolism. Thromb Haemost 2002;88407- 414
PubMed

Figures

Place holder to copy figure label and caption
Figure.

The clinical prediction rule. Cerebrovascular disease includes transient ischemic attack or stroke. Altered mental status includes disorientation, lethargy, stupor, or coma.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Predictor Variables Considered
Table Graphic Jump LocationTable 2. Baseline Patient Characteristics in the Derivation and Validation Samples*
Table Graphic Jump LocationTable 3. Risk Classification and Outcomes for Patients in the Derivation and Validation Samples*
Table Graphic Jump LocationTable 4. Accuracy of the Prediction Rule to Predict 30-Day Mortality in the Derivation and Validation Samples*

References

Anderson  FA  JrWheeler  HBGoldberg  RJ  et al.  A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism: the Worcester DVT Study. Arch Intern Med 1991;151933- 938
PubMed Link to Article
Silverstein  MDHeit  JAMohr  DNPetterson  TMO’Fallon  WMMelton  LJ  III Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study. Arch Intern Med 1998;158585- 593
PubMed Link to Article
Kozak  LJOwings  MFHall  MJ National Hospital Discharge Survey: 2002 annual summary with detailed diagnosis and procedure data. Vital Health Stat 13 2005;(158)1- 199
PubMed
Kurkciyan  IMeron  GSterz  F  et al.  Pulmonary embolism as a cause of cardiac arrest: presentation and outcome. Arch Intern Med 2000;1601529- 1535
PubMed Link to Article
Simonneau  GSors  HCharbonnier  B  et al. Thésée Study Group, A comparison of low-molecular-weight heparin with unfractionated heparin for acute pulmonary embolism. N Engl J Med 1997;337663- 669
PubMed Link to Article
Buller  HRDavidson  BLDecousus  H  et al. Matisse Investigators, Subcutaneous fondaparinux versus intravenous unfractionated heparin in the initial treatment of pulmonary embolism. N Engl J Med 2003;3491695- 1702[published correction appears in N Engl J Med. 2004;350:423]
PubMed Link to Article
Tapson  VFWitty  LA Massive pulmonary embolism: diagnostic and therapeutic strategies. Clin Chest Med 1995;16329- 340
PubMed
Wells  PSKovacs  MJBormanis  J  et al.  Expanding eligibility for outpatient treatment of deep venous thrombosis and pulmonary embolism with low-molecular-weight heparin: a comparison of patient self-injection with homecare injection. Arch Intern Med 1998;1581809- 1812
PubMed Link to Article
Kovacs  MJAnderson  DMorrow  BGray  LTouchie  DWells  PS Outpatient treatment of pulmonary embolism with dalteparin. Thromb Haemost 2000;83209- 211
PubMed
Wells  PSAnderson  DRRodger  MA  et al.  A randomized trial comparing 2 low-molecular-weight heparins for the outpatient treatment of deep vein thrombosis and pulmonary embolism. Arch Intern Med 2005;165733- 738
PubMed Link to Article
Wells  PS Outpatient treatment of patients with deep-vein thrombosis or pulmonary embolism. Curr Opin Pulm Med 2001;7360- 364
PubMed Link to Article
Janata  K Managing pulmonary embolism. BMJ 2003;3261341- 1342
PubMed Link to Article
British Thoracic Society Standards of Care Committee; Pulmonary Embolism Guideline Development Group, British Thoracic Society guidelines for the management of suspected acute pulmonary embolism. Thorax 2003;58470- 483
PubMed Link to Article
Auble  TEHsieh  MGardner  W  et al.  A prediction rule to identify low-risk patients with heart failure. Acad Emerg Med 2005;12514- 521
PubMed Link to Article
Douketis  JDKearon  CBates  SDuku  EKGinsberg  JS Risk of fatal pulmonary embolism in patients with treated venous thromboembolism. JAMA 1998;279458- 462
PubMed Link to Article
Goldhaber  SZVisani  LDe Rosa  M Acute pulmonary embolism: clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 1999;3531386- 1389
PubMed Link to Article
Carson  JLKelley  MADuff  A  et al.  The clinical course of pulmonary embolism. N Engl J Med 1992;3261240- 1245
PubMed Link to Article
Heit  JASilverstein  MDMohr  DNPetterson  TMO’Fallon  WMMelton  LJ  III Predictors of survival after deep vein thrombosis and pulmonary embolism: a population-based, cohort study. Arch Intern Med 1999;159445- 453
PubMed Link to Article
Grifoni  SOlivotto  ICecchini  P  et al.  Short-term clinical outcome of patients with acute pulmonary embolism, normal blood pressure, and echocardiographic right ventricular dysfunction. Circulation 2000;1012817- 2822
PubMed Link to Article
Wicki  JPerrier  APerneger  TVBounameaux  HJunod  AF Predicting adverse outcome in patients with acute pulmonary embolism: a risk score. Thromb Haemost 2000;84548- 552
PubMed
Giannitsis  EMuller-Bardorff  MKurowski  V  et al.  Independent prognostic value of cardiac troponin T in patients with confirmed pulmonary embolism. Circulation 2000;102211- 217
PubMed Link to Article
Kanich  WBrady  WJHuff  JS  et al.  Altered mental status: evaluation and etiology in the ED. Am J Emerg Med 2002;20613- 617
PubMed Link to Article
Peres Bota  DLopes Ferreira  FMelot  CVincent  JL Body temperature alterations in the critically ill. Intensive Care Med 2004;30811- 816
PubMed Link to Article
Umpierrez  GEIsaacs  SDBazargan  NYou  XThaler  LMKitabchi  AE Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab 2002;87978- 982
PubMed Link to Article
Hodkinson  HM Value of admission profile tests for prognosis in elderly patients. J Am Geriatr Soc 1981;29206- 210
PubMed
Herrmann  FRSafran  CLevkoff  SEMinaker  KL Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med 1992;152125- 130
PubMed Link to Article
Lee  CTGuo  HRChen  JB Hyponatremia in the emergency department. Am J Emerg Med 2000;18264- 268
PubMed Link to Article
Terzian  CFrye  EBPiotrowski  ZH Admission hyponatremia in the elderly: factors influencing prognosis. J Gen Intern Med 1994;989- 91
PubMed Link to Article
Steen  PMBrewster  ACBradbury  RCEstabrook  EYoung  JA Predicted probabilities of hospital death as a measure of admission severity of illness. Inquiry 1993;30128- 141
PubMed
Konstantinides  SGeibel  AOlschewski  M  et al.  Importance of cardiac troponins I and T in risk stratification of patients with acute pulmonary embolism. Circulation 2002;1061263- 1268
PubMed Link to Article
ten Wolde  MSohne  MQuak  EMac Gillavry  MRBuller  HR Prognostic value of echocardiographically assessed right ventricular dysfunction in patients with pulmonary embolism. Arch Intern Med 2004;1641685- 1689
PubMed Link to Article
Fine  MJAuble  TEYealy  DM  et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336243- 250
PubMed Link to Article
Mehr  DRBinder  EFKruse  RL  et al.  Predicting mortality in nursing home residents with lower respiratory tract infection: the Missouri LRI Study. JAMA 2001;2862427- 2436
PubMed Link to Article
van Gorp  MJSteyerberg  EWKallewaard  Mvan der Graaf  Y Clinical prediction rule for 30-day mortality in Bjork-Shiley convexo-concave valve replacement. J Clin Epidemiol 2003;561006- 1012
PubMed Link to Article
Douketis  JD Prognosis in pulmonary embolism. Curr Opin Pulm Med 2001;7354- 359
PubMed Link to Article
MacMahon  B The National Death Index. Am J Public Health 1983;731247- 1248
PubMed Link to Article
Breiman  LFriedman  JOlshen  R  et al.  Classification and Regression Trees.  Pacific Grove, Calif Wadsworth1984;
 S-Plus 2000 User’s Guide Data Analysis Products Division.  Seattle, Wash MathSoft2000;
Perrier  ARoy  PMAujesky  D  et al.  Diagnosing pulmonary embolism in outpatients with clinical assessment, D-dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study. Am J Med 2004;116291- 299
PubMed Link to Article
Aujesky  DSmith  KJCornuz  JRoberts  MS Cost-effectiveness of low-molecular-weight heparin for treatment of pulmonary embolism. Chest 2005;1281601- 1610
PubMed Link to Article
Nendaz  MRBandelier  PAujesky  D  et al.  Validation of a risk score identifying patients with acute pulmonary embolism, who are at low risk of clinical adverse outcome. Thromb Haemost 2004;911232- 1236
PubMed
White  RHGettner  SNewman  JMTrauner  KBRomano  PS Predictors of rehospitalization for symptomatic venous thromboembolism after total hip arthroplasty. N Engl J Med 2000;3431758- 1764
PubMed Link to Article
White  RHRomano  PSZhou  HRodrigo  JBargar  W Incidence and time course of thromboembolic outcomes following total hip or knee arthroplasty. Arch Intern Med 1998;1581525- 1531
PubMed Link to Article
Murin  SRomano  PSWhite  RH Comparison of outcomes after hospitalization for deep venous thrombosis or pulmonary embolism. Thromb Haemost 2002;88407- 414
PubMed

Correspondence

CME


You need to register in order to view this quiz.
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 78

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
PubMed Articles
JAMAevidence.com

Users' Guides to the Medical Literature
Thromboembolism or Acute Pulmonary Embolism

Users' Guides to the Medical Literature
Table 9.2-3 Refuted Evidence From Observational Studiesa