A Prediction Rule to Identify Low-Risk Patients with Community-Acquired Pneumonia
Michael J. Fine, M.D., Thomas E. Auble, Ph.D., Donald M. Yealy, M.D., Barbara H. Hanusa, Ph.D., Lisa A. Weissfeld, Ph.D., Daniel E. Singer, M.D., Christopher M. Coley, M.D., Thomas J. Marrie, M.D., and Wishwa N. Kapoor, M.D., M.P.H.
Background There is considerable variability in rates of hospitalizationof patients with community-acquired pneumonia, in part becauseof physicians' uncertainty in assessing the severity of illnessat presentation.
Methods From our analysis of data on 14,199 adult inpatientswith community-acquired pneumonia, we derived a prediction rulethat stratifies patients into five classes with respect to therisk of death within 30 days. The rule was validated with 1991data on 38,039 inpatients and with data on 2287 inpatients andoutpatients in the Pneumonia Patient Outcomes Research Team(PORT) cohort study. The prediction rule assigns points basedon age and the presence of coexisting disease, abnormal physicalfindings (such as a respiratory rate of >30 per minute ora temperature of >40°C), and abnormal laboratory findings(such as a pH <7.35, a blood urea nitrogen concentration>30 mg per deciliter [11 mmol per liter] or a sodium concentration<130 mmol per liter) at presentation.
Results There were no significant differences in mortality ineach of the five risk classes among the three cohorts. Mortalityranged from 0.1 to 0.4 percent for class I patients (P = 0.22),from 0.6 to 0.7 percent for class II (P = 0.67), and from 0.9to 2.8 percent for class III (P = 0.12). Among the 1575 patientsin the three lowest risk classes in the Pneumonia PORT cohort,there were only seven deaths, of which only four were pneumonia-related.The risk class was significantly associated with the risk ofsubsequent hospitalization among those treated as outpatientsand with the use of intensive care and the number of days inthe hospital among inpatients.
Conclusions The prediction rule we describe accurately identifiesthe patients with community-acquired pneumonia who are at lowrisk for death and other adverse outcomes. This prediction rulemay help physicians make more rational decisions about hospitalizationfor patients with pneumonia.
Community-acquired pneumonia is diagnosed in approximately 4million adults each year in the United States, and more than600,000 of these are hospitalized.1,2 The site of care —home or hospital — often determines the extensivenessof the diagnostic evaluation, the route of antimicrobial therapy,and the intensity of clinical observation. The aggregate costof hospitalization for the disease approaches $4 billion peryear.2,3,4
Hospital admission rates for pneumonia vary markedly from onegeographic region to the next,5,6,7 suggesting that the criteriaused for hospitalization are inconsistent. Physicians oftenrely on their subjective impressions of a patient's clinicalappearance in making the initial decision about the site ofcare.8 Physicians tend to overestimate the risk of death inpatients with pneumonia, and these overestimates are associatedwith the decision to hospitalize patients at low risk.8
Accurate, objective models of prognosis for community-acquiredpneumonia could help physicians assess patients' risks and improvethe decisions about hospitalization.9,10,11,12,13,14,15,16,17,18,19Previous models have been limited by retrospective design,11,14,15,19the use of predictor variables about which information is notreadily available to physicians when patients present,9,11,13,15,17,18,19and dependence on complex calculations that are difficult toapply in the clinical setting.19 The general applicability ofthese studies has been limited by the evaluations of performanceat single study sites,13,15,16 failure to validate findingsin independent patient populations,13,15,19 and a nearly exclusivefocus on hospitalized patients.10,11,13,14,15,19 Finally, clinicalrelevance has been compromised by a reliance on mortality asthe sole measure of patient outcomes.10,11,12,13,14,15,16,17,18,19
The purposes of this study were to develop a prediction rulefor prognosis that would accurately identify patients with community-acquiredpneumonia who are at low risk of dying within 30 days of presentationand to assess the predictive accuracy of this rule for clinicallyrelevant major outcomes.
Methods
Deriving the Prediction Rule
We derived a prediction rule for prognosis by analyzing dataon 14,199 adult inpatients with community-acquired pneumoniain the 1989 MedisGroups Comparative Hospital Database, whichcontains information on patients discharged from 78 hospitalsin 23 states. In the MedisGroups system, patients' charts areabstracted to collect data on more than 250 key clinical findingsrelating to demographics, history, physical examination, coexistingillnesses, laboratory results, and radiographic findings.20,21,22The MedisGroups admission review is based on the most-abnormalkey clinical findings on hospital day 1 or 2.
To be included in the derivation cohort, patients had to beat least 18 years of age and have a principal diagnosis of pneumoniaaccording to the International Classification of Diseases, 9thRevision, Clinical Modification (ICD-9-CM).18 We excluded patientswith a history of the acquired immunodeficiency syndrome ora positive titer of antibodies to the human immunodeficiencyvirus (HIV), as well as patients who had been hospitalized previouslywithin seven days before the current admission or transferredfrom another acute care hospital.9,17,18,19
We developed the prediction rule with 30-day hospital mortalityas the outcome. Patients in the derivation cohort who were dischargedor transferred from the hospital in less than 30 days or whoremained in the hospital for more than 30 days were consideredalive for this analysis.
Development of the prediction rule was based on a previouslyvalidated index that predicted 60-day mortality among patientswith community-acquired pneumonia.19 The following modificationswere made in the original index to improve ease of use and clinicalrelevance23,24,25: the follow-up interval was reduced from 60to 30 days to increase the proportion of deaths attributableto pneumonia,11,26,27 uncommonly ordered base-line laboratorytests were eliminated as predictor variables, residence in anursing home and the presence of renal and liver disease wereconsidered as potential predictor variables, predictor variableswith continuous and ordinal scales were converted into dichotomousvariables, and all interaction terms in the model were eliminated.
Finally, the prediction rule was developed in two steps to parallelmore closely physicians' decision-making processes. Step 1 wasdesigned to identify a subgroup of patients at low risk of deathsolely on the basis of their history and physical-examinationfindings. In step 2, the risk of death was quantified in theremaining patients with the same findings used in step 1 plusselected laboratory and radiographic data.
Candidate predictor variables analyzed in step 1 consisted ofthree demographic variables (age, sex, and nursing home residence),six coexisting illnesses (neoplastic disease, congestive heartfailure, cerebrovascular disease, coronary artery disease, renaldisease, and liver disease), and five physical-examination findings(pulse rate, respiratory rate, systolic blood pressure, temperature,and mental status). Significant predictors of mortality (P<0.05)were identified through logistic-regression analyses. The logisticmodel was used to rank patients according to their predictedprobability of death. On the basis of this ranking, patientswith the lowest risk of death were assigned to class I. Thesepatients had an observed cumulative mortality of less than 0.5percent and none of the independent predictors of mortalityidentified in step 1.
Candidate predictor variables analyzed in step 2 consisted ofthe 14 predictor variables considered in step 1 plus 7 laboratorymeasurements and radiographic findings (blood urea nitrogen,glucose, hematocrit, sodium, partial pressure of arterial oxygen,arterial pH, and pleural effusion). To generate a simple-integerpoint score, the logistic-regression–model coefficientsfor all statistically significant (P<0.05) predictors ofmortality in step 2 were divided by the coefficient for ageand rounded to the nearest multiple of 10, with one exception:abnormal temperature was assigned 15 points because temperaturesof less than 35.0°C and 40.0°C or higher had estimatesof 15 and 14 points, respectively. A total point score for eachpatient, reflecting the probability of death, was computed byadding the age in years (age minus 10 for women) and all additionalpoints for the documented predictor variables. After the totalpoint scores were calculated, patients were assigned to riskclass II, III, IV, or V. The cutoff for risk class II was thehighest total point score in which the observed cumulative mortalitywas less than 1.0 percent. Patients in risk class III had apredicted probability of death of less than 0.04, and patientsin risk classes IV and V had predicted probabilities of deathof 0.04 to 0.10 and greater than 0.10, respectively.
Validation of the Prediction Rule
The prediction rule was validated with data from a 1991 PennsylvaniaMedisGroups statewide data base on 38,039 adult patients hospitalizedwith community-acquired pneumonia. The data base contains informationabout patients discharged from 193 general medical and surgicalhospitals in Pennsylvania. The methods used to collect informationon key clinical findings and identify patients with pneumoniain this data base corresponded directly to the methods usedin the 1989 MedisGroups cohort.28
The prediction rule was also validated with data on patientsenrolled in the Pneumonia PORT prospective cohort study. Thisobservational study of outpatients and inpatients with community-acquiredpneumonia was conducted at five medical institutions: the Universityof Pittsburgh Medical Center and St. Francis Medical Center,in Pittsburgh; Massachusetts General Hospital and Harvard CommunityHealth Plan–Kenmore Center, in Boston; and Victoria GeneralHospital, in Halifax, Nova Scotia, Canada.
To be included in the Pneumonia PORT cohort study, patientshad to be at least 18 years of age, have one or more symptomssuggestive of pneumonia, have radiographic evidence of pneumoniawithin 24 hours of presentation, and provide informed consentfor base-line and follow-up interviews. Patients were ineligiblefor the study if they had been discharged from an acute carehospital within 10 days before presentation for pneumonia orwere known to be HIV-positive.
During the study enrollment period (October 1991 to March 1994),4002 persons who satisfied all the criteria for study eligibilitywere identified, of whom 2287 (57.1 percent) were enrolled.The leading reason for the nonenrollment of eligible patientswas patients' or physicians' refusal to participate (43.3 percentof those not enrolled). Enrolled patients were younger thaneligible nonenrolled patients (mean age, 56 years vs. 61 years)and were more often classified as being at low risk for mortalityin the short term (68.9 percent vs. 57.8 percent).
Data on the 21 predictor variables considered in the derivationof the prediction rule were collected through chart review andpatient interviews. In contrast to the data from the MedisGroupsdata bases, the information on vital signs and laboratory valuesrepresented the first values available to physicians after patientpresentation, rather than the most-abnormal results obtainedwithin the first 48 hours after presentation, and coexistingillnesses were defined according to predetermined clinical definitionsrather than ICD-9-CM diagnosis codes.
Patients in the Pneumonia PORT cohort study were followed prospectivelyto assess their vital status and a variety of outcomes 30 daysafter the radiographic diagnosis of pneumonia. For all the patientswho died, underlying and immediate causes of death were assignedindependently by two investigators29; disagreements were resolvedby the consensus of a panel of five investigators using a standardprotocol.26 Deaths were defined as pneumonia-related if pneumoniawas designated as the underlying or immediate cause of deathor was determined to have had a major contributing role in thecause of death.26,29 For outpatients, all subsequent hospitalizationswere recorded. For all inpatients and outpatients who were subsequentlyhospitalized, admission to an intensive care unit for hemodynamicinstability, respiratory failure, or mechanical ventilationduring the index hospitalization was recorded. For all inpatientsdischarged alive, the length of their hospital stay was measured.
Statistical Analysis
Three methods were used to validate the prediction rule. Mortalityrates in each of the five risk classes were compared in thederivation and validation cohorts with the use of chi-squarestatistics. The areas beneath the receiver-operating-characteristiccurves for predicting mortality in each of the five risk classeswere compared in the derivation and validation cohorts.30,31The associations between risk class and other medical outcomeswere assessed with the use of the Cochran–Armitage testfor trend32 for subsequent hospitalization and admission toan intensive care unit and with a test for trend in survivalcurves for length of stay.33 For all analyses, a two-tailedP value of less than 0.05 was considered to indicate statisticalsignificance.
Results
Patients' Characteristics
Patients in the Pneumonia PORT cohort were younger and had alower prevalence of coexisting illnesses and fewer abnormalfindings on physical examination and laboratory tests than patientsin the two MedisGroups cohorts, reflecting the younger age andlower prevalence of coexisting illnesses among the outpatientsin the Pneumonia PORT cohort (Table 1). Mortality in the MedisGroupsderivation and validation cohorts was 10.2 and 10.6 percent,respectively (P = 0.24). Overall mortality was lower in thePneumonia PORT cohort than in both MedisGroups cohorts (P<0.001for both comparisons), primarily because of the 0.6 percentmortality among outpatients.
Table 1. Demographic and Clinical Characteristics of the Patients in the Derivation and Validation Cohorts.
Derivation of the Prediction Rule
In step 1 of the prediction rule, the following were independentlyassociated with mortality: an age of more than 50 years, eachof five coexisting illnesses (neoplastic disease, congestiveheart failure, cerebrovascular disease, renal disease, and liverdisease), and each of five physical examination findings (alteredmental status; pulse, >125 per minute; respiratory rate,>30 per minute; systolic blood pressure, <90 mm Hg; andtemperature, <35°C or >40°C). Of the 14,199 patientsin the derivation cohort, 9.7 percent with none of these 11risk factors were assigned to risk class I (Figure 1).
Figure 1. Identifying Patients in Risk Class I in the Derivation of the Prediction Rule.
In step 1 of the prediction rule, the following were independently associated with mortality: an age of more than 50 years, five coexisting illnesses (neoplastic disease, congestive heart failure, cerebrovascular disease, renal disease, and liver disease), and five physical-examination findings (altered mental status; pulse, 125 per minute; respiratory rate, 30 per minute; systolic blood pressure, <90 mm Hg; and temperature, <35°C or 40°C). In the derivation cohort, 1372 patients (9.7 percent) with none of these 11 risk factors were assigned to risk class I. All 12,827 remaining patients were assigned to risk class II, III, IV, or V according to the sum of the points assigned in step 2 of the prediction rule (see Tables 2 and 3).
In step 2, in addition to the 11 factors identified in step1, 2 demographic factors (male sex and nursing home residence)and 7 laboratory or radiographic findings (blood urea nitrogenconcentration, >30 mg per deciliter [11 mmol per liter];glucose concentration, >250 mg per deciliter [14 mmol perliter]; hematocrit, <30 percent; sodium concentration, <130mmol per liter; partial pressure of oxygen, <60 mm Hg; arterialpH, <7.35; and pleural effusion) were each independentlyassociated with mortality in the remaining 12,827 patients.The point scoring system shown in Table 2 was used to measurethe magnitude of the association of each of these 20 factorswith mortality.
Table 2. Point Scoring System for Step 2 of the Prediction Rule for Assignment to Risk Classes II, III, IV, and V.
Validation of the Prediction Rule
No significant differences in mortality in each of the fiverisk classes were found among the three study cohorts (Table 3).Mortality was low for risk classes I, II, and III, rangingfrom 0.1 to 0.4 percent for class I, from 0.6 to 0.7 percentfor class II, and from 0.9 to 2.8 percent for class III. Therewas no significant difference (P = 0.15) in the area under thereceiver-operating-characteristic curves between the MedisGroupsderivation cohort (0.84) and the MedisGroups validation cohort(0.83). Although the area under the curve was significantlygreater in the Pneumonia PORT cohort (0.89) than in either ofthe MedisGroups cohorts (P<0.001), the absolute differencesin area were minimal.
Table 3. Comparison of Risk-Class–Specific Mortality Rates in the Derivation and Validation Cohorts.
Of the 1575 Pneumonia PORT patients in the three lowest riskclasses, only 7 died (1 in class I, 3 in class II, and 3 inclass III). Only 4 of these deaths were pneumonia-related: 3in patients with terminal cancer and 1 in a patient with obstructivepulmonary disease, alcoholism, and malnutrition. None of thesedeaths were judged to have been preventable.
There was a significant relation between risk class and eachof the other medical outcomes evaluated in the Pneumonia PORTcohort (Table 4). Among outpatients, the rate of subsequenthospitalization within 30 days ranged from 5.1 percent for classI patients to 20.0 percent for class IV (P<0.001). None ofthe 62 class I, II, or III outpatients who were subsequentlyhospitalized died, and only 1 was admitted to an intensive careunit. Of the eight outpatients in classes IV or V who were subsequentlyhospitalized, three died and one was admitted to an intensivecare unit.
Table 4. Medical Outcomes in the Pneumonia PORT Cohort According to Risk Class.
Among inpatients, admissions to intensive care units rangedfrom 4.3 percent for class I to 17.3 percent for class V (P<0.001).For all 1236 inpatients who were discharged alive, the proportionwho stayed in the hospital three days or fewer was 26.1 percentfor class I and 3.7 percent for class V (P<0.001).
The clinical profiles of patients within risk classes were nearlyidentical in the three study cohorts (*). Class I patients wereall young (median age, 35 to 37 years) and had none of the pertinentcoexisting illnesses or abnormalities on physical examination.Class II patients were typically middle-aged (median age, 58to 59 years), and most were assigned to this class by virtueof their age alone. Class III patients were typically older(median age, 72), and most had at least one pertinent coexistingillness, one physical-examination abnormality, or one laboratoryor radiographic abnormality. Class IV and V patients were somewhatolder (median age, >75) and were virtually never assignedto these classes by virtue of their age alone; the majorityhad abnormalities in two (class IV) or all three (class V) ofthe pertinent risk factor categories.
Discussion
In comparison with previous prognostic models for community-acquiredpneumonia,9,10,11,12,13,14,15,16,17,18,19 our prediction rulehas distinctive strengths.23,24,25,34 First, the predictor variablesare all explicitly defined and can be readily assessed at thetime of patient presentation. Second, patients can be assignedto the lowest risk class (class I) on the basis of informationfrom the initial history and physical examination alone, whichpermits physicians to avoid ordering laboratory tests that arecostly and often difficult to perform in an outpatient setting.Third, the accuracy and generalizability of the rule are supportedby its derivation and validation in over 50,000 inpatients from275 hospitals across the United States and Canada. Finally,validation in the Pneumonia PORT cohort allowed assessment ofthe rule in outpatients, follow-up for mortality after hospitalizationfor those treated as inpatients, and examination of additionalmedical outcomes that are critical to fully evaluating the prognosisfor patients with pneumonia.
The prognosis for patients with community-acquired pneumoniaranges from rapid recovery to death.35 The great variabilityseen in rates of hospital admission and lengths of stay forpneumonia in part reflects uncertainty among physicians in assessingthe severity of this illness and the perceived benefits of hospitalcare.5,6,7,36 Our prediction rule was designed to reduce suchuncertainty and to foster more appropriate use of hospitalsin the management of this illness.
The prediction rule identifies three distinct risk classes (I,II, and III) of patients who are at sufficiently low risk fordeath and other adverse medical outcomes that physicians canconsider outpatient treatment or an abbreviated course of inpatientcare for them. All patients 50 years of age or less who havenone of the coexisting illnesses or physical-examination abnormalitiesidentified in step 1 of the rule (class I) should be candidatesfor outpatient treatment. Many patients in risk classes II andIII are also potential candidates for outpatient treatment.This strategy should apply to the majority of patients assignedto these two risk classes by virtue of age alone or the presenceof a single pertinent coexisting illness or abnormal findingon physical examination or laboratory testing. For the remainingpatients in classes II and III for whom treatment at home withoral antimicrobial therapy is judged to be unsuitable, thereare alternatives to traditional inpatient care. These includeparenteral antimicrobial therapy at home or a short stay (<24hours) in a hospital observation unit. Previous studies havesuggested that one fifth of all patients hospitalized with pneumoniaremain in the hospital after becoming medically stable.37 Therisk stratification provided by our rule could also help targetlow-risk patients at the time of admission for whom rapid conversionfrom intravenous to oral antimicrobial therapy38,39,40,41,42and early discharge43 might be appropriate.
The potential impact of this prediction rule can be estimatedby using projections from the Pneumonia PORT cohort. A strategyof outpatient care for all class I and II patients, brief inpatientobservation for patients in class III, and traditional inpatientcare for all patients in classes IV and V would have reducedthe proportion of patients receiving traditional inpatient careby 31 percent and meant a brief observational hospital stayfor an additional 19 percent of those who were treated as inpatients.Of the Pneumonia PORT inpatients who would have been recommendedfor outpatient care if this strategy had been used, fewer than1 percent died (3 patients) and 4.3 percent (18 patients) wereadmitted to an intensive care unit.
An additional margin of safety could be provided by amendingthis strategy to include traditional inpatient care for allpatients in classes I, II, and III who have hypoxemia at presentation(i.e., who have an oxygen saturation of less than 90 percentor a partial pressure of oxygen of less than 60 mm Hg whilebreathing room air). Special attention to oxygenation statusis consistent with published criteria for hospitalization andwith actual clinical practice8,44; in the Pneumonia PORT cohortstudy, 99 percent of the patients known to have hypoxemia atpresentation were hospitalized. Under this amended strategy,the proportion of patients who received traditional inpatientcare would still have been reduced by 26 percent, and an additional13 percent of inpatients would have been treated with a briefobservational hospital stay. Of the inpatients for whom outpatientcare would have been recommended according to this strategy,mortality was the same (three patients), and only 1.6 percent(four patients) were admitted to an intensive care unit. Withboth of the strategies we have described, inpatient care wouldhave been recommended for five of the six patients treated inthe outpatient setting who died (all in class IV). Given theprevalence of this illness, strategies that reduce the use oftraditional hospital care could result in large aggregate costsavings. Furthermore, reducing the rate of hospitalization oflow-risk patients with pneumonia is consistent with the clearpreferences of patients for treatment at home rather than inthe hospital.45
We must address the potential limitations of our predictionrule before recommending its use in clinical practice. First,patients designated as being at low risk may have importantmedical and psychosocial contraindications to outpatient care.For example, administering oral antibiotics in an outpatientsetting to patients with intractable vomiting is not an option.8Likewise, patients who use intravenous drugs or who are alcoholicor unreliable or have severe psychiatric conditions may requirehospitalization to ensure compliance with treatment. Finally,patients with severely impaired cognitive function who are unableto carry out activities of daily living independently and thosewith little social support may require traditional inpatientcare regardless of the severity of their illness.
Second, some patients have rare conditions, such as severe neuromusculardisease or immunosuppression, that are not included as predictorsin our model but that clearly increase the likelihood of a pooroutcome. In such cases, our rule would not supersede a physician'sjudgment.
Third, the rule was constructed with dichotomous predictor variables(abnormal vs. normal) to facilitate its use in practice. Asa result, it may oversimplify the way physicians interpret thepredictor variables. For example, a clinician would be unlikelyto discharge a previously healthy 25-year-old patient with severehypotension and tachycardia and no additional pertinent prognosticfactors, despite the patient's having a class II designationaccording to the rule.
In conclusion, we derived and validated a prediction rule thatidentifies patients with community-acquired pneumonia who areat low risk for death and other adverse outcomes. Our projectionsfrom the observational Pneumonia PORT cohort provide preliminaryevidence that one or more strategies for applying this rulecould safely reduce the need for hospitalization in the treatmentof patients with pneumonia. However, it is important to notethat the premise that a large proportion of low-risk inpatientscould be treated safely in an outpatient setting or with veryshort hospital stays assumes that the processes of care in thehospital are not critical determinants of medical outcomes amonglow-risk patients. Although this study provides preliminaryevidence that our prediction rule could help physicians determinewhen hospital care is appropriate for patients with community-acquiredpneumonia, firm recommendations for its clinical use will dependon future prospective trials to confirm its effectiveness andsafety.
This work was conducted as part of the Pneumonia PORT project,funded by a grant from the Agency for Health Care Policy andResearch (RO1 HS-06468). Dr. Fine is a Robert Wood Johnson FoundationGeneralist Physician Faculty Scholar.
* See NAPS document no. 05359 for one page of supplementary material.To order, contact NAPS c/o Microfiche Publications, 248 HempsteadTpk., West Hempstead, NY 11552.
Source Information
From the Division of General Medicine, Department of Medicine (M.J.F., B.H.H., W.N.K.), the Department of Emergency Medicine (T.E.A., D.M.Y.), and the Department of Biostatistics (L.A.W.), Graduate School of Public Health, University of Pittsburgh, Pittsburgh; the General Internal Medicine Unit, Medical Services, Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S., C.M.C.); and the Division of Infectious Diseases, Department of Medicine, Victoria General Hospital and Dalhousie University, Halifax, N.S., Canada (T.J.M.).
Address reprint requests to Dr. Fine at Montefiore University Hospital, 8 East Rm. 824, 200 Lothrop St., Pittsburgh, PA 15213.
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(2006). Decision Rules and Pneumonia: What Are We "Predicting," and for Whom?. Am. J. Respir. Crit. Care Med.
174: 1169-1170
[Full Text]
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125 per minute; respiratory rate,