Abstract
Despite careful evaluation of changes in hospital care for community-acquired pneumonia (CAP), little is known about intensive care unit (ICU) use in the treatment of this disease. There are criteria that define CAP as “severe,” but evaluation of their predictive value is limited. We compared characteristics, course, and outcome of inpatients who did (n = 170) and did not (n = 1,169) receive ICU care in the Pneumonia Patient Outcomes Research Team prospective cohort. We also assessed the predictive characteristics of four prediction rules (the original and revised American Thoracic Society criteria, the British Thoracic Society criteria, and the Pneumonia Severity Index [PSI]) for ICU admission, mechanical ventilation, medical complications, and death (as proxies for severe CAP). ICU patients were more likely to be admitted from home and had more comorbid conditions. Reasons for ICU admission included respiratory failure (57%), hemodynamic monitoring (32%), and shock (16%). ICU patients incurred longer hospital stays (23.2 vs. 9.1 days, p < 0.001), higher hospital costs ($21,144 vs. $5,785, p < 0.001), more nonpulmonary organ dysfunction, and higher hospital mortality (18.2 vs. 5.0%, p < 0.001). Although ICU patients were sicker, 27% were of low risk (PSI Risk Classes I–III). Severity-adjusted ICU admission rates varied across institutions, but mechanical ventilation rates did not. The revised American Thoracic Society criteria rule was the best discriminator of ICU admission and mechanical ventilation (area under the receiver operating characteristic curve, 0.68 and 0.74, respectively) but none of the prediction rules were particularly good. The PSI was the best predictor of medical complications and death (area under the receiver operating characteristic curve, 0.65 and 0.75, respectively), but again, none of the prediction rules were particularly good. In conclusion, ICU use for CAP is common and expensive but admission rates are variable. Clinical prediction rules for severe CAP do not appear adequately robust to guide clinical care at the current time.
The appropriate management of community-acquired pneumonia (CAP) has received close attention in the current era of rising health care costs (1, 2). Considerable efforts have been made to shorten unnecessary hospital length of stay (LOS) and optimize the initial decision to hospitalize (3–7). However, most of these efforts were designed to reduce unnecessary care for less sick patients. Less attention has been paid to patients with severe CAP, such as those requiring care in an intensive care unit (ICU). Several authors studied CAP in ICU patients but focused mainly on microbiologic etiology (8, 9) or short-term mortality (8–12). Few studies compared patients managed with and without ICU care (11, 13–20), and those that did were generally of small sample size, were not recent, were from outside North America, or provided few data comparing the two groups.
In a 1993 Consensus Statement designed to standardize and improve care, the American Thoracic Society (ATS) defined a subset of CAP as “severe” on the basis of the presence of specific risk factors, or criteria, and recommended that ICU admission be considered for these patients (21). These criteria were evaluated in one study from Spain (22) and reported in an abstract from one North American study (23). Both studies suggested the definition of severe CAP was overly sensitive and nonspecific. In response, a second ATS consensus panel modified the definition of severe CAP, based in part on the Spanish classification of risk factors as major or minor (22), and recommended evaluation of these revised criteria (24). There are two other clinical prediction rules for CAP: the British Thoracic Society (BTS) criteria (25) and the Pneumonia Severity Index (PSI) (6). The relative merits of these different rules have not been assessed in a common data set.
The goal of this article is twofold: first, to provide a description of differences in baseline characteristics, processes of care, and medical outcomes between hospitalized patients who do and do not receive ICU care in the Pneumonia Patient Outcomes Research Team (PORT) prospective cohort, a North American cohort enrolled from 1991 to 1994; and second, to evaluate the predictive characteristics of the original and revised ATS criteria, the BTS criteria, and the PSI for ICU admission, mechanical ventilation, medical complications, and death—four proxies for “severe” CAP.
We studied the inpatients of the Pneumonia PORT cohort study (4) at three U.S. and one Canadian sites. Patients were ⩾ 18 years of age, had clinical and radiographic evidence of pneumonia within 24 hours of presentation, and provided informed consent. We assessed characteristics through chart review and interviews, using standardized data collection instruments (6, 26, 27). We quantified severity of illness by using the PSI (6).
We assessed hospital length of stay (LOS) and cost (U.S. sites only; determined from detailed billing records [28]), ICU use, mechanical ventilation use, laboratory investigations, and antibiotic therapy. We collected data on all medical complications within 30 days of presentation. We considered worsening of chronic conditions as a complication. We defined shock as a systolic blood pressure < 90 mm Hg despite fluid resuscitation or vasopressor requirement. We noted the development of acute organ dysfunction for each of six organ systems as defined previously (see expanded Methods in the online data supplement) (29).
We determined survival at 30 and 90 days. Two investigators independently reviewed detailed case summaries of all deaths, based on medical record review and interviews with caregivers and family members, and assigned the cause of death (30) according to World Health Organization criteria (31). We recorded return to work for those previously employed and return to usual activities.
We determined the presence of each of the seven original ATS risk factors (tachypnea, respiratory failure, mechanical ventilation, bilateral or multilobar pneumonia by chest radiograph, shock, vasopressor therapy, and renal impairment, as previously defined; see expanded Methods in the online data supplement [21]) and the three BTS risk factors (respiratory rate ⩾ 30/minute, diastolic blood pressure < 60 mm Hg, and serum urea > 7 mM) (25) at baseline.
To define severe CAP by original ATS criteria, any one of the seven risk factors must be present. To define severe CAP by revised ATS criteria, two of three minor criteria (systolic blood pressure ⩽ 90 mm Hg, multilobar disease, or PaO2/FiO2 < 250) or one of two major criteria (mechanical ventilation or shock) must be present (22). To define severe CAP by the BTS criteria, any two of the three risk factors must be present (25).
We compared categorical data using the χ2 statistic or the Fisher exact test (32) and continuous data using the Student t test (33) or Mantel–Cox log-rank test (34). We compared time to return to work and time to return to usual activities by Kaplan–Meier estimation (35). We built logistic regression models to compare severity-adjusted ICU admission rates and mechanical ventilation rates across centers (34).
To determine how well the prediction rules predicted an episode of CAP that was “severe,” we determined the relative risk (as a measure of association between the risk factor and outcome), and sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves (as measures of discrimination) for four events: ICU admission, mechanical ventilation, development of a medical complication, and death. We dichotomized the PSI as low (Classes I–III) or high (Classes IV and V) risk for these analyses. We used the values at hospital admission for each criteria assessment. We assumed statistical significance at p < 0.05 and conducted analyses in SAS (SAS Institute, Cary, NC) and SPSS (SPSS, Chicago, IL).
Of the 1,339 inpatients in the study cohort, 12.7% (n = 170) were admitted to the ICU, with ICU admission rates ranging from 8.8 to 26.1% across participating centers (p = 0.005). We found higher ICU admission rates for patients admitted from home, patients who were unemployed, patients with a history of substance abuse, and patients with underlying disease. We found lower ICU admission rates for patients admitted from nursing homes, patients with prior “do not resuscitate” orders, and patients with dementia (Table 1)
Characteristic | n | ICU Admissions (%, n)* | p Value |
|---|---|---|---|
| Cohort | 1,339 | 12.7, 170 | |
| Living arrangements | |||
| Private residence, alone | 275 | 13.1, 36 | 0.011 |
| Private residence, with others | 815 | 14.5, 118 | |
| Nursing home/chronic care facility | 184 | 6.5, 12 | |
| Other† | 64 | 6.3, 4 | |
| Employment status | |||
| Employed | 218 | 7.8, 17 | 0.017 |
| Not employed | 1,118 | 13.7, 153 | |
| Significant comorbid conditions | |||
| Chronic pulmonary disease‡ | 451 | 15.5, 70 | 0.029 |
| Coronary artery disease | 349 | 16.6, 58 | 0.010 |
| Alcohol or intravenous drug abuse | 260 | 16.5, 43 | < 0.001 |
| Congestive heart failure | 225 | 19.1, 43 | 0.002 |
| Renal disease | 139 | 18.0, 25 | 0.049 |
| Dementia | 133 | 3.8, 5 | 0.001 |
| None of the above | 327 | 5.5, 18 | < 0.001 |
| Number of comorbid conditions | |||
| 0 | 224 | 7.1, 16 | < 0.001 |
| 1 | 301 | 11.0, 33 | |
| 2 or 3 | 568 | 14.1, 80 | |
| ⩾ 4 | 246 | 16.7, 41 | |
| Do-not-resuscitate orders at presentation | 199 | 5.5, 11 | 0.001 |
| Severity of illness (PSI) | |||
| Risk Class I | 184 | 6.0, 11 | < 0.001 |
| Risk Class II | 233 | 5.6, 13 | |
| Risk Class III | 253 | 8.7, 22 | |
| Risk Class IV | 446 | 15.9, 71 | |
| Risk Class V | 223 | 23.8, 53 |
Patients admitted to the ICU were more likely to complain of dyspnea and have tachypnea, tachycardia, hypothermia, or altered mental status at presentation than non-ICU patients (see Table E1 in the online data supplement). There were no differences, however, in the total number of symptoms, symptom bother, or severity of symptom scores between the two groups (see Table E2 in the online data supplement). Abnormal laboratory values were also more common and the chest radiograph was more likely to show extensive disease (see Table E1 in the online data supplement). The etiologic pattern was similar in both ICU and non-ICU patients (p = 0.19). Only Streptococcus pneumoniae (14.7%), Haemophilus influenzae (4.7%), and Staphylococcus aureus (4.1%) were reported at a rate of > 2% in ICU patients. A specific organism was more commonly identified in ICU patients but less than half of either group had a microbiologic etiology (44.7 vs. 33.3%, p = 0.002). Patients with high risk of death (PSI Risk Classes IV and V) were more likely to be admitted to the ICU. However, 27% (n = 46) of the ICU admissions were for patients classified as low risk at presentation (Risk Classes I–III) (Table 1).
As might be expected, ICU patients received a more aggressive diagnostic work-up than the non-ICU patients, including significantly more frequent gram stains, sputum culture, pleural taps, bronchoscopies, and serologic studies (data not shown) (p < 0.05 for each comparison). Antibiotic management was also considerably more intense for ICU patients, with ICU patients receiving twice the number of antibiotics as non-ICU patients (4.2 ± 2.2 vs. 2.6 ± 1.3, p < 0.001). Virtually all classes of antibiotics were prescribed more commonly in the ICU (see Table E3 in the online data supplement).
ICU patients incurred longer overall hospital LOS than non-ICU patients (23.2 ± 26.5 vs. 9.1 ± 9.3 days, p < 0.001), with a mean ICU LOS of 7.1 days (median, 3 days). Hospital costs (available for 846 U.S. patients) were also significantly higher, with median costs of $21,144 versus $5,785 for ICU and non-ICU patients, respectively (p < 0.001). This difference was due to increased LOS (see above), higher daily costs (see Table E4 in the online data supplement), and differences in survival (see below). The higher daily costs were seen across all cost centers and did not simply reflect the increased costs of an ICU bed (Figure 1)
Figure 1. Median daily hospital costs (U.S. dollars) by survivor and ICU cohort. Costs were available for 846 U.S. patients. See METHODS in the online data supplement for details regarding calculation of hospital costs.
[More] [Minimize]In a multivariate regression model for ICU admission, which had good fit (C statistic of 2.24 with 5 degrees of freedom, p = 0.81), mechanical ventilation before admission, respiratory failure, tachypnea, renal impairment, and vasopressor requirement were independently predictive. Using this model to control for differences in case mix, significant differences in ICU admission rates persisted across sites. We constructed a similar model to predict the use of mechanical ventilation, again with good fit (C statistic of 2.19 with 4 degrees of freedom, p = 0.7). Independent predictors of mechanical ventilation were respiratory failure, tachypnea, and vasopressor requirement. Of interest, although there was a twofold variation in unadjusted mechanical ventilation rates across sites (range, 28–56%; p = 0.03), there was no difference after controlling for case mix.
The ICU patients had a severalfold increase in most pulmonary and nonpulmonary complications compared with non-ICU patients (see Table E5 in the online data supplement). The cardiac complications were particularly notable, with half of all patients developing shock (47.6%), half showing signs of worsened congestive heart failure (51.2%), and a quarter developing atrial arrhythmias (26.5%). Anemia (28.8%), abnormal liver function tests (30.0%), and renal impairment (32.4%) were also common in ICU patients. This trend to higher complications in those admitted to the ICU is further reflected in the distribution of acute nonpulmonary organ dysfunction, as shown in Figure 2
Figure 2. Distribution of nonrespiratory acute organ dysfunction in patients with and without ICU care. See METHODS in the online data supplement for definitions of acute organ dysfunction.
[More] [Minimize]Medical outcomes are detailed in Table 2
Characteristic | Non-ICU | ICU | p Value |
|---|---|---|---|
| Hospital mortality, % | |||
| All | 5.0 | 18.2 | < 0.001* |
| Risk Class I | 0.0 | 0.0 | NA |
| Risk Class II | 0.9 | 7.7 | 0.16 |
| Risk Class III | 0.4 | 4.6 | 0.17 |
| Risk Class IV | 5.1 | 21.1 | < 0.001 |
| Risk Class V | 21.2 | 26.4 | 0.43 |
| Pneumonia as major cause of hospital death, % of deaths | 74.2 | 73.1 | 0.91 |
| Mortality by 30 days, % | 6.9 | 15.3 | < 0.001 |
| Mortality by 90 days, % | 13.0 | 24.7 | < 0.001 |
| Discharge location, % of hospital survivors | |||
| Home | 83.1 | 71.0 | < 0.001 |
| Nursing home | 16.7 | 22.9 | |
| Other institution | 0.3 | 6.1 | |
| RTW by 30 days,† % of those who worked before onset of pneumonia | 70.5 | 25.9 | 0.019 |
| Time for patients to RTW, median (days) | 21 | — | |
| RTUA by 30 days,‡ % | 65.0 | 38.3 | < 0.001 |
| Time for patients to RTUA, median (days) | 20 | — | |
| Hospital readmission within 30 days of presentation, % | 10.5 | 6.9 | 0.25 |
Figure 3. Kaplan–Meier survival curves for ICU and non-ICU patients. Survival was lower in ICU patients (p < 0.05).
[More] [Minimize]The predictive characteristics of baseline individual ATS risk factors, the original ATS criteria (any one risk factor), the revised ATS criteria, the BTS criteria, and high-risk PSI scores (PSI Risk Classes IV and V) are presented in Table 3
Event* | Sensitivity (%) | Specificity (%) | ROC (95% CI) | PPV (%) | NPV (%) | RR (95% CI) |
|---|---|---|---|---|---|---|
| ICU admission | ||||||
| Presence of ATS risk factor | ||||||
| Respiratory rate† | 34.1 | 82.7 | 22.3 | 89.6 | 2.1 (1.5–3.1) | |
| Respiratory failure‡ | 56.5 | 69.5 | 21.2 | 91.6 | 2.5 (1.8–3.5) | |
| Mechanical ventilation | 6.5 | 100.0 | 100.0 | 88.0 | 8.4 (4.4–15.7) | |
| Bilateral/multilobe X-ray§ | 37.1 | 73.7 | 17.0 | 89.0 | 1.5 (1.1–2.2) | |
| Shock | 4.7 | 96.9 | 18.2 | 87.5 | 1.5 (0.7–3.2) | |
| Vasopressor therapy‖ | 10.0 | 96.3 | 28.3 | 88.0 | 2.4 (1.3–4.3) | |
| Renal impairment# | 20.0 | 93.5 | 30.9 | 88.9 | 2.8 (1.8–4.3) | |
| Original ATS criteria | 81.8 | 43.1 | 0.61 (0.57–0.65) | 17.3 | 94.2 | 3.0 (2.0–4.5) |
| Revised ATS criteria | 70.7 | 72.4 | 0.68 (0.64–0.73) | 26.4 | 94.7 | 4.9 (3.4–7.1) |
| BTS criteria | 39.6 | 78.2 | 0.58 (0.53–0.63) | 20.2 | 90.3 | 2.1 (1.5–2.9) |
| High PSI (Risk Class IV or V) | 72.9 | 53.4 | 0.60 (0.56–0.65) | 18.5 | 93.1 | 2.7 (1.9–3.9) |
| Mechanical ventilation** | ||||||
| Original ATS criteria | 86.2 | 42.3 | 0.64 (0.58–0.69) | 10.2 | 97.6 | 4.2 (2.3–7.6) |
| Revised ATS criteria | 100.0 | 72.8 | 0.74 (0.69–0.79) | 21.9 | 100.0 | †† |
| BTS criteria | 51.1 | 78.0 | 0.64 (0.58–0.71) | 15.0 | 95.4 | 3.3 (2.1–5.0) |
| High PSI (Risk Class IV or V) | 53.8 | 50.5 | 0.63 (0.58–0.69) | 7.6 | 93.6 | 1.2 (0.8–1.8) |
| Medical complication | ||||||
| Original ATS criteria | 69.2 | 71.1 | 0.60 (0.57–0.64) | 89.1 | 40.4 | 1.5 (1.1–2.0) |
| Revised ATS criteria | 67.4 | 62.2 | 0.60 (0.57–0.63) | 84.1 | 39.1 | 1.3 (1.0–1.7) |
| BTS criteria | 28.3 | 86.6 | 0.57 (0.54–0.60) | 83.8 | 33.1 | 1.3 (0.9–1.7) |
| High PSI (Risk Class IV or V) | 58.0 | 77.3 | 0.65 (0.61–0.68) | 89.7 | 35.1 | 1.4 (1.0–1.9) |
| Death** | ||||||
| Original ATS criteria | 79.8 | 41.4 | 0.60 (0.54–0.65) | 8.8 | 96.6 | 2.6 (1.5–4.5) |
| Revised ATS criteria | 39.6 | 67.6 | 0.63 (0.57–0.69) | 8.2 | 93.9 | 1.3 (0.9–2.1) |
| BTS criteria | 56.0 | 78.4 | 0.62 (0.55–0.68) | 15.9 | 96.1 | 4.0 (2.6–6.2) |
| High PSI (Risk Class IV or V) | 94.4 | 53.2 | 0.75 (0.71–0.78) | 12.6 | 99.3 | 16.8 (6.8–41.8) |
In predicting ICU admission, individual risk factors were generally specific, with high negative predictive value, but insensitive, with poor positive predictive value. This is because each risk factor usually required ICU admission, but there were many different risk factors. For example, most patients who require vasopressors are admitted to the ICU, but there are many patients admitted to the ICU who are not receiving vasopressors.
In contrast to individual risk factors, the different clinical prediction rules generally had better sensitivity for ICU admission, because they captured more than one possible cause for ICU admission. The revised ATS criteria had the best overall discrimination, as measured by ROC curves, but none of the rules were particularly good (ranging from 0.58 to 0.68, where 0.5 occurs by random chance alone) because many non-ICU patients met criteria. For example, 60% (n = 804) of all inpatients met original ATS criteria for severe CAP, 83% of whom (n = 665) were never admitted to an ICU. One-third (n = 440) of all inpatients met the revised ATS criteria, 74% of whom (n = 324) were never admitted to an ICU; 24% (n = 321) of all inpatients met the BTS criteria, 80% of whom (n = 256) were never admitted to an ICU. The revised ATS criteria were a good discriminator for the need for mechanical ventilation, whereas the PSI was a good discriminator for death. The performance of the different rules was consistent across hospitals (Figure 4
Figure 4. Discriminative power of the revised ATS and BTS prediction rules for severe CAP across hospitals. Point estimates and 95% confidence intervals for the area under the receiver operating characteristic (ROC) curve of the revised ATS and the BTS rules in predicting ICU admission, death, and mechanical ventilation. Higher numbers for the area under the ROC curve indicate better discriminative power.
[More] [Minimize]Despite the considerable attention to CAP, comparatively little is known about current ICU use in the treatment of this disease. Our study demonstrated several important points. Not surprisingly, ICU patients were sicker, as reflected by several baseline criteria, and had poorer outcomes and greater resource use. Although the number of patients receiving ICU care was only a small proportion of all patients with CAP, they consumed more than one-third of all hospital costs for CAP. The likelihood of receiving ICU care was poorly predicted by most measures of severity, raising the possibility that the ICU admission decision may be rather discretionary and influenced by local practice patterns. In contrast, mechanical ventilation rates did not vary across institutions after adjusting for severity of illness, suggesting this decision is less discretionary and more closely linked to the patient's severity of illness.
Given the high cost of ICU care and the considerable variation in ICU admission decisions, a closer examination of how patients are admitted is warranted. Prior studies of CAP also suggested a wide variation in ICU admission rates, ranging from 3 to 39% (10, 36). Suboptimal decision-making regarding ICU admission could result in under- or overuse of the ICU, with potential consequences including adverse outcomes due to delayed or inadequate care for some patients and excessive resource use for other patients. For example, low risk of death as predicted by the PSI (Risk Classes I, II, and III) has been proposed as a reason to deny hospital admission, yet one-quarter of the ICU patients in our cohort were in these classes.
Our study focused on the relationship between the ATS criteria and subsequent care decisions (ICU admission and mechanical ventilation) and outcomes (medical complications and death) that might define CAP as “severe,” while comparing them with other prognostic instruments. Our results suggest that all the rules were associated with the events suggestive of severe CAP, but their discrimination appeared too low to guide individual decision-making. The biggest problem was the poor positive predictive value. For example, three-quarters of the patients who met any of the criteria were never admitted to the ICU. As reported previously, the original ATS criteria, although sensitive, had low specificity. Unfortunately, at least in this cohort, the improvement in predictive ability of the revised ATS criteria was modest. The BTS criteria, attractive because of their simplicity, performed less well than the revised ATS criteria.
Other ICU risk prediction methods, such as APACHE III (37), do have good predictive characteristics for ICU course and outcome but are generally not available at the time of the decision to admit a patient to the ICU. It may be worthwhile exploring whether elements from such scores, measured before admission, enhance the predictive accuracy of the PSI or ATS criteria. Any attempt to study and improve the ICU admission decision ought also to standardize the type and level of care offered in the ICU and in the alternative to the ICU (e.g., the floor) if the benefits of ICU care are to be best understood. Our data further suggest that outcome should be assessed beyond hospital discharge if the full economic and clinical burden of disease is to be captured.
We identified an etiologic agent in less than half of the ICU patients. In other studies of patients with CAP requiring admission to the ICU, a microbiologic etiology was determined in 58–72% of patients (8, 9, 38). The lower rate in our study may have been because the ordering of microbiologic cultures and serologies was at the discretion of the clinical team. As with our study, other investigators (8–12, 36, 38) have shown that the spectrum of etiologic agents of pneumonia in patients admitted to ICU is similar to that in the general population of patients with CAP, although the frequency of these pathogens varies across studies.
Nonpulmonary complications were common among the ICU-treated patients. In a study of 299 patients with severe CAP, Leroy and coworkers (39) noted the development of similar nonpulmonary complications but generally at lower rates than those observed in our study. Torres and coworkers (40) studied 92 patients with severe CAP requiring ICU treatment and reported a high incidence of acute renal failure, septic shock, cardiac dysrhythmias, and abnormal liver function tests, similar to our study. These data support the need to provide multisystem care to patients with CAP requiring ICU care.
There are limitations to our study. The Pneumonia PORT cohort is one of the largest, most detailed, and most contemporary studies of CAP. However, data were collected in the early and mid 1990s at four institutions. Thus, care patterns may not be representative of current care at other North American sites. For example, the high use of second-generation cephalosporins, although consistent with the 1993 ATS consensus statement on CAP (21), would be considered inappropriate today. On the other hand, the extent to which current practice is compliant with the latest recommendations is unclear. Also, there was considerable variation in practice across institutions, yet the predictive characteristics of the different rules were robust to these variations.
We determined only which factors were associated with a higher likelihood of receiving ICU care, and not which factors were associated with a higher likelihood of benefit from ICU care. We also examined only patient characteristics that influence the ICU admission decision, yet other factors, such as bed availability or family preferences, may affect the admission decision. We did not have culture sensitivity data and could not therefore analyze how management was influenced by the appropriateness of initial antibiotic choice, an important variable affecting CAP management (41). Finally, there is no gold standard for the term “severe CAP.” We therefore presented results defining severe CAP in four ways (i.e., CAP with one of four separate specific events). We chose these events on the basis of clinical face validity, but recognize that the definitions are arbitrary.
In summary, although overshadowed in numbers by patients with low-risk CAP, patients who receive ICU care represent an important subset, both in terms of cost and morbidity. The current use of ICU services for CAP is expensive and may be somewhat discretionary with outcomes that, although reasonable, require measurement beyond hospital discharge to be fully understood. Existing risk predictors will likely require modification before they can be used to guide individual ICU admission decisions, but such work is essential if ICU services are to be used optimally.
The authors thank research nurses Rhonda Grandy, R.N., Dawn Menon, G.N., Jackie Cunning, R.N., Linda Kraft, R.N., and Maxine Young, R.N., in Halifax; Mary Walsh, R.N., Donna Polenik, R.N., M.P.H., and Kathryn Fine, R.N. in Pittsburgh; Mary Ungaro, R.N., Leila Haddad, A.B., M.P.H., and Marian Hendershot, R.N. in Boston. The authors also thank Walter T. Linde-Zwirble for thoughtful review and comments.
| 1. | Graves EJ, Gillum BS. 1994 Summary: National Hospital Discharge Survey, advance data. Washington, DC: National Center for Health Statistics; 1996. Report No. 278. |
| 2. | Dahmash NS, Chowdhury MN. Re-evaluation of pneumonia requiring admission to an intensive care unit: a prospective study. Thorax 1994; 49:71–76. |
| 3. | McCormick D, Fine MJ, Coley CM, Marrie TJ, Lave JR, Obrosky DS, Kapoor WN, Singer DE. Variation in length of hospital stay in patients with community-acquired pneumonia: are shorter stays associated with worse medical outcomes? Am J Med 1999;107:5–12. |
| 4. | Halm EA, Fine MJ, Marrie TJ, Coley CM, Kapoor WN, Obrosky DS, Singer DE. Time to clinical stability in patients hospitalized with community-acquired pneumonia: implications for practice guidelines. JAMA 1998;279:1452–1457. |
| 5. | Ramirez JA, Srinath L, Ahkee S, Huang A, Raff MJ. Early switch from intravenous to oral cephalosporins in the treatment of hospitalized patients with community-acquired pneumonia. Arch Intern Med 1995; 155:1273–1276. |
| 6. | Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ, Kapoor WN. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997; 336:243–250. |
| 7. | Atlas SJ, Benzer TI, Borowsky LH, Chang Y, Burnham DC, Metlay JP, Halm EA, Singer DE. Safely increasing the proportion of patients with community-acquired pneumonia treated as outpatients: an interventional trial. Arch Intern Med 1998;158:1350–1356. |
| 8. | British Thoracic Society Research Committee, Public Health Laboratory Service. The aetiology, management and outcome of severe community-acquired pneumonia in the intensive care unit. Respir Med 1992; 86:7–13. |
| 9. | Moine P, Vercken JB, Chevret S, Gajdos P. Severe community-acquired pneumococcal pneumonia. French Study Group of Community-Acquired Pneumonia in ICU. Scand J Infect Dis 1995;27:201–206. |
| 10. | Ortqvist AB, Sterner G, Nilsson JA. Severe community-acquired pneumonia: factors influencing need of intensive care treatment and prognosis. Scand J Infect Dis 1985;17:377–386. |
| 11. | van Eeden SF, Coetzee AR, Joubert JR. Community-acquired pneumonia: factors influencing intensive care admission. S Afr Med J 1988;73: 77–81. |
| 12. | Sorensen J, Cederholm I, Carlsson C. Pneumonia: a deadly disease despite intensive care treatment. Scand J Infect Dis 1986;18:329–335. |
| 13. | Ortqvist AB, Hedlund J, Grillner L, Jalonen E, Kallings I, Leinonen M, Kalin M. Aetiology, outcome and prognostic factors in community-acquired pneumonia requiring hospitalization. Eur Respir J 1990;3:1105–1113. |
| 14. | Ortqvist AB, Grepe A, Julander I, Kalin M. Bacteremic pneumococcal pneumonia in Sweden: clinical course and outcome and comparison with non-bacteremic pneumococcal and mycoplasmal pneumonias. Scand J Infect Dis 1988;20:163–171. |
| 15. | Gransden WR, Eykyn SJ, Phillips I. Pneumococcal bacteraemia: 325 episodes diagnosed at St Thomas's Hospital. BMJ 1985;290:505–508. |
| 16. | Marrie TJ. Bacteraemic pneumococcal pneumonia: a continuously evolving disease. J Infect 1992;24:247–255. |
| 17. | van Eeden SF, de Beer P. Community-acquired pneumonia: evidence of functional inactivation of α1 proteinase inhibitor. Crit Care Med 1990;18:1204–1209. |
| 18. | Puren AJ, Feldman C, Savage N, Becker PJ, Smith C. Patterns of cytokine expression in community-acquired pneumonia. Chest 1995;107:1342–1349. |
| 19. | Lippmann ML, Goldberg SK, Walkenstein MD, Herring W, Gordon M. Bacteremic pneumococcal pneumonia: a community hospital experience. Chest 1995;108:1608–1613. |
| 20. | Marfin AA, Sporrer J, Moore PS, Siefkin AD. Risk factors for adverse outcome in persons with pneumococcal pneumonia. Chest 1995;107: 457–462. |
| 21. | Niederman MS, Bass JBJ, Campbell GD, Fein AM, Grossman RF, Mandell LA, Marrie TJ, Sarosi GA, Torres A, Yu VL. Guidelines for the initial management of adults with community-acquired pneumonia: diagnosis, assessment of severity, and initial antimicrobial therapy. American Thoracic Society. Am Rev Respir Dis 1993;148:1418–1426. |
| 22. | Ewig S, Ruiz M, Mensa J, Marcos MA, Martinez JA, Arancibia F, Niederman MS, Torres A. Severe community-acquired pneumonia: assessment of severity criteria. Am J Respir Crit Care Med 1998;158:1102–1108. |
| 23. | Gordon GS, Throop D, Berberian L, Niederman M, Bass JBJ, Alemayehu D, Mellis S. Validation of the therapeutic recommendations of the American Thoracic Society (ATS) guidelines for community-acquired pneumonia in hospitalized patients. Chest 2000;110:55S. |
| 24. | Niederman MS, Mandell LA, Anzueto A, Bass JB, Broughton WA, Campbell GD, Dean N, File T, Fine MJ, Gross PA, et al. Guidelines for the management of adults with community-acquired pneumonia: diagnosis, assessment of severity, antimicrobial therapy, and prevention. Am J Respir Crit Care Med 2001;163:1730–1754. |
| 25. | British Thoracic Society. Guidelines for the management of community-acquired pneumonia in adults admitted to hospital. Br J Hosp Med 1993;49:346–350. |
| 26. | Fine MJ, Stone RA, Singer DE, Coley CM, Marrie TJ, Lave JR, Hough LJ, Obrosky DS, Schulz R, Ricci EM, et al. Processes and outcomes of care for patients with community-acquired pneumonia: results from the Pneumonia Patient Outcomes Research Team (PORT) cohort study. Arch Intern Med 1999;159:970–980. |
| 27. | Metlay JP, Fine MJ, Schulz R, Marrie TJ, Coley CM, Kapoor WN, Singer DE. Measuring symptomatic and functional recovery in patients with community-acquired pneumonia. J Gen Intern Med 1997;12:423–430. |
| 28. | Fine MJ, Pratt HM, Obrosky DS, Lave JR, McIntosh LJ, Singer DE, Coley CM, Kapoor WN. Relation between length of hospital stay and costs of care for patients with community-acquired pneumonia. Am J Med 2000;109:378–385. |
| 29. | Clermont G, Angus DC, Linde-Zwirble WT, Griffin MF, Fine MJ, Pinsky MR. Does acute organ dysfunction predict patient-centered outcomes? Chest 2002;121:1963–1971. |
| 30. | Mortensen EM, Coley CM, Singer DE, Marrie TJ, Obrosky DS, Kapoor WN, Fine MJ. Causes of death for patients with community-acquired pneumonia: results from the Pneumonia Patient Outcomes Research Team (PORT) cohort study. Arch Intern Med 2002;162:1059–1064. |
| 31. | World Health Organization. Manual of the international statistical classification of diseases, injuries and causes of death. Geneva, Switzerland: World Health Organization; 1977. |
| 32. | Kendall M, Stuart A. The advanced theory of statistics: design and analysis, and time-series. New York: Macmillan; 1979. p. 580–585. |
| 33. | Feinstein AR. Clinical epidemiology. Philadelphia, PA: WB Saunders; 1985. p. 145–153. |
| 34. | Norman GR, Streiner DL. Biostatistics: the bare essentials. St. Louis, MO: Mosby-Year Book; 1993. p. 190–191. |
| 35. | Norman GR, Streiner DL. Biostatistics: the bare essentials. St. Louis, MO: Mosby-Year Book; 1993. p. 186–187. |
| 36. | Fang GD, Fine MJ, Orloff JJ, Arisumi D, Yu VL, Kapoor WN, Grayston JT, Wang SP, Kohler R, Muder RR, et al. New and emerging etiologies for community-acquired pneumonia with implications for therapy: a prospective multicenter study of 359 cases. Medicine (Baltimore) 1990; 69:307–316. |
| 37. | Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano AM, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:1619–1636. |
| 38. | Arancibia F, Ewig S, Martinez JA, Ruiz M, Bauer T, Marcos MA, Mensa J, Torres A. Antimicrobial treatment failures in patients with community-acquired pneumonia: causes and prognostic implications. Am J Respir Crit Care Med 2000;162:154–160. |
| 39. | Leroy O, Santre C, Beuscart C, Georges H, Guery B, Jacquier JM, Beaucaire G. A five-year study of severe community-acquired pneumonia with emphasis on prognosis in patients admitted to an intensive care unit. Intensive Care Med 1995;21:24–31. |
| 40. | Torres A, Serra-Batlles J, Ferrer A, Jimenez P, Celis R, Cobo E, Rodriguez-Roisin R. Severe community-acquired pneumonia: epidemiology and prognostic factors. Am Rev Respir Dis 1991;144:312–318. |
| 41. | Gleason PP, Kapoor WN, Stone RA, Lave JR, Obrosky DS, Schulz R, Singer DE, Coley CM, Marrie TJ, Fine MJ. Medical outcomes and antimicrobial costs with the use of the American Thoracic Society guidelines for outpatients with community-acquired pneumonia. JAMA 1997;278:32–39. |
