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Original Contribution |

Predicting Mortality in Nursing Home Residents With Lower Respiratory Tract Infection:  The Missouri LRI Study FREE

David R. Mehr, MD, MS; Ellen F. Binder, MD; Robin L. Kruse, PhD; Steven C. Zweig, MD, MSPH; Richard Madsen, PhD; Lori Popejoy, MSN, RN; Ralph B. D'Agostino, PhD
[+] Author Affiliations

Author Affiliations: Center for Family Medicine Science, Department of Family and Community Medicine, School of Medicine (Drs Mehr, Kruse, and Zweig), Department of Statistics (Dr Madsen), and Sinclair School of Nursing (Ms Popejoy), University of Missouri, Columbia; Department of Internal Medicine, Division of Geriatrics and Gerontology, Washington University School of Medicine, St Louis, Mo (Dr Binder); and Department of Mathematics and Statistics, Boston University, Boston, Mass (Dr D'Agostino).


JAMA. 2001;286(19):2427-2436. doi:10.1001/jama.286.19.2427.
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Published online

Context Lower respiratory tract infection (LRI) is a leading cause of mortality and hospitalization in nursing home residents. Treatment decisions may be aided by a clinical prediction rule that identifies residents at low and high risk of mortality.

Objective To identify patient characteristics predictive of 30-day mortality in nursing home residents with an LRI.

Design, Setting, and Patients Prospective cohort study of 1406 episodes of LRI in 1044 residents of 36 nursing homes in central Missouri and the St Louis, Mo, area between August 15, 1995, and September 30, 1998.

Main Outcome Measure Thirty-day all-cause mortality.

Results Thirty-day mortality was 14.7% (n = 207). In a logistic analysis, using generalized estimating equations to adjust for clustering, we developed an 8-variable model to predict 30-day mortality, including serum urea nitrogen, white blood cell count, body mass index, pulse rate, activities of daily living status, absolute lymphocyte count of less than 800/µL (0.8 × 109/L), male sex, and deterioration in mood over 90 days. In validation testing, the model exhibited reasonable discrimination (c = .76) and calibration (nonsignificant Hosmer-Lemeshow goodness-of-fit statistic, P = .54). A point score based on this model's variables fit to the entire data set closely matched observed mortality. Fifty-two percent of residents had low (score of 0-4) or relatively low (score of 5-6) predicted 30-day mortality, with 2.2% and 6.2% actual mortality, respectively.

Conclusions Our model distinguishes nursing home residents at relatively low risk for mortality due to LRI. If independently validated, our findings could help physicians identify nursing home residents in need of different therapeutic approaches for LRI.

Figures in this Article

Pneumonia and the somewhat broader category of lower respiratory tract infection (LRI), which includes pneumonia, bronchitis, and tracheobronchitis, are the leading causes of mortality and hospitalization among nursing home residents.14 In recent outcome studies, 30-day mortality from pneumonia or LRI ranged between 11.4% and 30%.59 Clinical findings consistently associated with mortality have included functional dependence (defined by activities of daily living [ADLs]) and elevated respiratory rate.5,7,10,11

Because many nursing home residents are chronically ill and near the end of life, the first step in making treatment decisions should be determining appropriate therapeutic measures (eg, aggressive care, limited curative treatment, or strictly palliative care). For strictly palliative care, maximizing comfort should guide treatment. Otherwise, clinicians need to determine illness severity to decide on specific treatment and whether residents should be treated in the nursing home or hospital. Residents at low risk for mortality may be excellent candidates for nursing home management, which may prevent complications from hospitalization.12,13 For community-acquired pneumonia, the validated Pneumonia Severity Index (PSI) provides guidance.14 However, because of its broad scope, most nursing home residents would be classified in its 2 highest-risk categories.

In a large prospective sample of nursing home residents with LRI, we derived and validated a new predictive model to better distinguish residents at low risk of dying. We chose 30-day mortality as the most useful outcome for considering treatment decisions, which is consistent with the approach of other researchers.14

We identified participants from 36 nursing homes in central Missouri and the St Louis, Mo, area. Facility characteristics were similar to 1997 national averages.15 For example, 64% were for profit vs 67% nationally. Forty-seven percent of the facilities had fewer than 100 beds, 44% had between 100 and 199, and 8% had 200 or more, whereas facilities nationwide had 50%, 42%, and 8%, respectively. Thirty-one percent of the facilities were in nonmetropolitan areas vs 38% nationally.

Definition of LRI

We chose to study mortality from LRI rather than pneumonia to make our findings most relevant to nursing home practice. Physicians caring for nursing home patients frequently do not obtain chest radiographs, and the clinical distinction between bronchitis or tracheobronchitis and pneumonia is difficult, even though the conditions are pathologically distinct. Thus, LRI includes pneumonia and other LRI (BOX 1). The definition is a modification of a surveillance definition for long-term care facilities.16

Box 1. Lower Respiratory Tract Infection (LRI) Definition*

An LRI was defined to include either pneumonia or other LRI. Both of the following criteria were required for pneumonia:

  • Interpretation of a chest radiograph as demonstrating pneumonia or probable pneumonia. If a previous radiograph exists for comparison, the infiltrate should be new.

  • At least 2 of the LRI symptoms and signs below are present.

All 3 of the following criteria were required for other LRI (bronchitis, tracheobronchitis):

  • Pneumonia as defined above is absent or no chest radiograph is available.

  • At least 3 of the LRI signs and symptoms below are present.

  • In the presence of chronic obstructive pulmonary disease or congestive heart failure, additionally, the resident must have a temperature of ≥38°C for the illness to qualify as an LRI.

LRI symptoms and signs used in the definition:

  • New or increased cough

  • New or increased sputum production

  • Fever (≥38°C)

  • Pleuritic chest pain

  • New or increased physical findings on chest examination (rales, rhonchi, wheezes, bronchial breathing)

  • One of the following indications of change in status or breathing difficulty: new/increased shortness of breath, or respiratory rate greater than 25/min, or worsening mental or functional status (significant deterioration in the resident's cognitive status or in the resident's ability to carry out the activities of daily living, respectively).

*Based on the statement of a consensus development conference concerning infection-surveillance definitions for long-term care facilities.16 We modified the definition to explicitly exclude residents with chronic obstructive pulmonary disease or congestive heart failure who lacked either a fever or probable pneumonia on chest radiograph to avoid including congestive heart failure or chronic obstructive pulmonary disease exacerbations as an LRI.

Patient Identification and Evaluation

Project nurses called or visited facilities at least 6 days per week to identify residents who had respiratory (eg, cough, sputum production) or nonspecific (eg, fever, acute confusion) symptoms compatible with an LRI. The nurses were also available by pager, and facility staff and physicians were encouraged to report ill residents at other times. Under a physician-authorized protocol, residents with such symptoms received a focused history and physical examination by a trained project nurse within 24 hours and usually on the same day. Most evaluations included a chest radiograph, complete blood count, and a chemistry panel. Project nurses predominantly had advanced-practice education or extensive clinical experience and training in physical assessment. Since evaluations were authorized by attending physicians, who also received clinical information regarding each case, they were considered part of appropriate care. Therefore, institutional review boards at each institution approved a simplified consent process using a simple acceptance or refusal of the evaluation as part of medical care. Potential cases were identified from August 15, 1995, through September 30, 1998. However, all facilities were not involved until December 1997. Additional details of resident identification and evaluation are described elsewhere.17

Of the 4959 illness episodes reported by nursing homes, project nurses performed 2592 evaluations to determine whether to include the episode in the study. We did not evaluate (hereafter excluded) residents who accounted for a total of 1191 episodes because they did not have lower respiratory or systemic symptoms or signs except for cough (Figure 1). We also excluded 1176 episodes in which residents were (1) ineligible because they were younger than 60 years old, not in the facility at least 14 days, or had taken an antibiotic in the last 7 days for a previous LRI; (2) not appropriate for an outcomes study because they had a "no antibiotics" order, were not expected to live more than 30 days, were enrolled in hospice, or had acquired immunodeficiency syndrome; (3) cared for by a physician not participating in the protocol or the resident, family, or physician declined a specific evaluation; or (4) identified too late for a timely evaluation (>48 hours after treatment was initiated). Some episodes were excluded for more than 1 reason. We compared age and vital signs between the 2592 evaluations and the 724 episodes that would have qualified for an evaluation but were excluded because of lack of permission for evaluation or because of late notification (categories 3 and 4 above). Age, pulse, and respiratory rate were not significantly different, but average temperature was slightly higher in those not evaluated (37.4°C vs 37.2°C; P = .002).

Figure. Exclusion Criteria and Pathway to Study Sample
Graphic Jump Location
LRI indicates lower respiratory tract infection. Asterisk indicates some episodes were excluded for more than 1 reason so the sum exceeds the total number shown.
Data Collection and Measures

Clinical evaluations of nursing home residents were recorded on standardized forms and placed in the medical record. When an LRI seemed likely, project nurses collected additional data using the nursing home Minimum Data Set (MDS),18,19 which is a reliable instrument when used by trained nurse assessors.20 From hospital (for residents who were hospitalized) and nursing home records, we obtained the following: active diagnoses and studies pertaining to diagnosis (for example, urinalysis and cultures of blood, urine, or sputum); oxygen therapy; immunization information; medications, including antibiotics, psychotropic drugs, and respiratory drugs; prior diagnoses; and prior hospital use. In 9.2% of evaluations, the resident was transferred to the hospital before project nurses could complete a physical assessment. In these instances, we obtained vital sign and clinical examination data from hospital records. Vital sign data used in the analysis were those obtained by the project nurse or, if not available, those first obtained at the hospital (usually the emergency department record). We chose the first available laboratory data after the resident qualified for evaluation.

From the MDS, we obtained data on depression and delirium; height and weight; other diagnoses and conditions (including pressure ulcers); use of devices, such as restraints; and the Cognitive Performance Scale (CPS),21 which measures cognitive impairment. We measured ADL dependency, by summing self-performance scores for 4 ADL items (grooming, using the toilet, locomotion, and eating) from the MDS (MDS ADL [Short Form], scale range of 0-16).22 In the final multivariable analyses, we simplified this to a 0 to 4 scale by counting the number of these 4 ADL items in which the individual was rated as either dependent or required extensive assistance. Consistent with MDS instructions, we evaluated ADL and cognitive status for the week prior to evaluation; delirium symptoms (MDS section B5)19 include an indication of new onset or worsening.

We ascertained survival or mortality from all causes at 30 days for all residents. Project nurses returned to nursing homes at 30 days to reassess functional status in living residents. In the few instances in which residents had moved, we followed up on their status at their new location. In the 3 instances in which this was not possible, we performed a death certificate search.

Radiographic Classification and Case Review

Chest radiographs were obtained in 2337 of the 2592 evaluations. We chose to evaluate radiology reports rather than reviewing all radiographs because only the report is typically available to clinicians. Based on defined criteria, 2 clinicians independently classified radiology reports into 3 categories: negative, possible, or probable (this group includes definite pneumonia). For example, according to these criteria, a report describing "new left lower lobe infiltrate suggestive of pneumonia" is probable pneumonia, while a report indicating "possible infiltrate" or "infiltrate suggestive of pneumonia or congestive heart failure" is possible pneumonia. In St Louis, 2 clinicians evaluated the reports, and in central Missouri 2 of 4 clinicians considered each report. When disagreement occurred, all 6 raters at the 2 sites independently reviewed the reports and attempted to reach consensus. In 11.7% of cases, consensus either could not be achieved, or was for possible pneumonia when only probable pneumonia would have qualified the episode for inclusion as an LRI under the study definition. In those instances, an additional radiologist independently interpreted the actual radiographs.

Following abstraction of all clinical information and final radiographic classification, project geriatricians (D.R.M., E.F.B., and S.C.Z.) reviewed clinical information from all evaluations to make a final determination of whether an episode met our case definition. In addition to 1117 episodes that did not meet the LRI definition (Figure 1), we found an additional 43 that technically met our definition but were not included as LRI cases because another illness or combination of illnesses was more likely (including 36 in which there was a documented urinary tract infection).

An additional 26 episodes were dropped from our analytic sample; in 23 there were inadequate data on predictor variables and 3 residents had 2 episodes of LRI in a 30-day interval during which they died. Since death should only be attributed to 1 episode, we excluded the second episode in these 3 instances.

Statistical Analyses

Data imputation was used for missing data since in developing multivariable models, data imputation is recommended as less biased than dropping cases.23 In this study, imputing mean values for missing continuous data and the largest category value for missing dichotomous variables was as efficient as more complicated procedures for imputation. Episodes were then randomly assigned to a 70% development (n = 975) and 30% validation (n = 431) sample. Selecting candidate variables and model building were restricted to the development data until a final variable reduction step.

The initial step in variable selection was based on the literature and clinical relevance, as judged by the 3 geriatrician investigators. A list of 25 categories of variables that might be related to mortality was constructed, including demographic factors (age, sex, race), vital signs (pulse, respiratory rate, temperature, blood pressure), findings of delirium (eg, acute confusion, decreased alertness), cognitive status, nutritional status (weight, body mass index [BMI], total lymphocyte count), physical function (ADL status and other mobility indicators), indicators of depression, comorbid conditions (eg, congestive heart failure, chronic obstructive pulmonary disease, stroke), and other laboratory findings (eg, white blood cell count, serum urea nitrogen, serum sodium).

We then considered descriptive and bivariable statistics describing the relationship of specific symptoms and examination findings to 30-day mortality. Using S-Plus software,24 continuous variables were examined with smoothed plots showing the shape of the relationship between the variable and mortality. Based on clinical relevance and statistical considerations, we then took the best representatives from these 25 categories of variables for consideration in building our multivariable model.25 We excluded 2 indicators of nutritional status, albumin and cholesterol, because of excessive missing data (35% and 48%, respectively). Changes in Medicare regulations during the study precluded physicians from ordering a comprehensive chemistry panel in nursing home residents with a possible LRI. Consistent with contemporary standards of care, most subjects did not receive an arterial blood gas or pulse oximetry.

We used forward and backward stepwise logistic regression to consider combinations of variables for inclusion in our final model (using P = .10 as an initial criterion for statistical significance). We used generalized estimating equations to adjust logistic regression estimates for 2 kinds of correlation within our data: individuals nested within facilities and participants represented by more than 1 episode.26 As few individuals had more than 4 episodes of LRI, we restricted the generalized estimating equations analysis to 4 or fewer episodes to avoid unstable estimates.

In testing continuous variables in these models, we considered the shape of the variable's relationship to mortality. For example, temperature exhibits a minimum mortality with a slight elevation of temperature and higher mortality with both high and low temperatures. Therefore, we tested linear and quadratic terms as well as using dummy variables to represent low, midrange, and high temperatures. We also limited the range of continuous variables to avoid undue influence of outliers. For example, serum urea nitrogen was set to 10 if less than 10 and to 80 if more than 80. In making final decisions on model inclusion, we considered clinical meaningfulness and the gain in discrimination by including a variable as measured by the c statistic and the Aikake Information Criterion (both available through SAS statistical software).27 We also reconsidered key variables based on the literature, such as age and respiratory rate, which had not been retained in stepwise selection procedures.

The result of these analyses was an 11-variable model. Because this was an excessive number of variables for the size of our validation data set, prior to the final model validation, we drew 5 other random samples from the entire data set. Three of the 11 variables originally fit to the development sample (low temperature, congestive heart failure on chest radiograph, and bilateral infiltrate on chest radiograph) improved discrimination in only half of the 6 samples, so they were dropped from the model.

We then used coefficients for the 8-variable model, as estimated in the development sample, to test the model's discrimination and calibration in the original validation sample.28 To assess discrimination, we primarily used the c statistic, which evaluates among all possible pairs of individuals whether those with higher predictive risk are more likely to die. The c statistic is also equal to the area under the receiver operating characteristic curve. The Hosmer-Lemeshow goodness-of-fit statistic was used to measure calibration by assessing agreement between predicted and observed risk by decile of predicted risk.29

Finally, the 8-variable model was fit to the entire data set and used to create an approximation in the form of a simple scoring system for clinicians. The predicted probability of mortality associated with each point total was computed by averaging predicted probability from this logistic model for all episodes with a given point total. Statistical analyses were performed with S-Plus24 and SAS statistical software.27

Project nurses evaluated residents in 2592 episodes with symptoms or signs suggesting an LRI. From these evaluations, we identified 1406 episodes in 1044 individuals for inclusion in our outcome analysis. Figure 1 shows how we derived our sample. Most residents (n = 794) had a single episode, 176 had 2 episodes, 48 had 3 episodes, 18 had 4 episodes, and 8 had more than 4 episodes. In all but 37 of the 1406 episodes, chest radiographs were available. Based on the assessments of radiographic reports, 186 (13.2%) had possible pneumonia and 748 (53.2%) had probable pneumonia. There were 207 deaths (14.7%) from all causes within 30 days, with 143 in the nursing home, 62 in the hospital, and 2 in an extended care unit following hospitalization. Nineteen percent were hospitalized within 48 hours and 27% were hospitalized within 30 days.

Table 1 shows selected characteristics of the development and validation samples at the onset of the LRI episode. Of note, 75% of episodes occurred in subjects who were older than 80 years. Table 2 and Table 3 show the bivariable relationship of selected variables to 30-day mortality in our entire sample. A large number of variables are associated with 30-day mortality, including most factors seen in previous studies.

Table Graphic Jump LocationTable 1. Characteristics of Nursing Home Residents With 1406 Episodes of Lower Respiratory Tract Infections*
Table Graphic Jump LocationTable 2. Bivariable Relationship of Selected Signs and Symptoms With 30-Day Mortality in the Entire Data Set*
Table Graphic Jump LocationTable 3. Bivariable Relationship of Conditions With 30-Day Mortality in the Entire Data Set*
Multivariable Analysis

Based on clinical and statistical considerations, we selected an 8-variable model of 30-day LRI mortality, including serum urea nitrogen, white blood cell count, BMI, pulse rate, ADL score, low total lymphocyte count (<800/µL [0.8 × 109/L]), male sex, and decline in mood over 90 days. Table 4 shows estimates derived using generalized estimating equations for the entire data set. As shown in Table 5, the model fit to the developmental sample showed good discrimination (c = 0.82) and calibration (Hosmer-Lemeshow goodness-of-fit statistic P = .85 with nonsignificant values indicating acceptable calibration). When the coefficients from the developmental sample were applied to the validation sample, discrimination declined (c = 0 .76) but calibration remained acceptable (P = .54). The validation sample estimate is more likely to be representative of the model's discriminating ability in an independent sample. Another useful measure of discrimination is the ratio of mortality in the highest-risk and lowest-risk quintiles as predicted by the model. In the development set this ratio was 17.2, and in the validation set it was 13.8. In contrast to these findings, testing of the 11-variable model showed that although it performed well in the development data (c = 0.83 and Hosmer-Lemeshow statistic P = 0.35), it did not perform as well in the validation set (c = 0.74 and P = .001, which indicates poor calibration).

Table Graphic Jump LocationTable 4. Predictors of 30-Day Mortality in Residents With 4 or Fewer Episodes: Generalized Estimating Equations Analysis*
Table Graphic Jump LocationTable 5. Measures of Calibration and Discrimination for Generalized Estimating Equations Analysis of 30-Day Mortality in Development and Validation Data Sets and All Data Combined*
Simplified Clinical Prediction Rule

We used the logistic model based on the entire data set to develop a simplified risk score, which approximates our logistic model, and can be more easily applied by clinicians (Table 6 and BOX 2). Table 7 shows how individual scores correspond to average predicted probabilities (from the logistic model) and observed mortality. The left portion of the table shows the risk score applied to the entire data set. Table 7 also shows how a similar score based on the logistic model from the development set performs in the development and validation sets.

Box 2. Numerical Example of the Missouri Lower Respiratory Tract Infection (LRI) Project Risk Score

Consider a hypothetical male nursing home resident with an LRI who exhibits the following: serum urea nitrogen of 20 mg/dL (7.14 mmol/L); white blood cell count of 8000/µL (8.0 × 109/L) with 15% lymphocytes; pulse of 80/min; requires extensive assistance in hygiene and locomotion, limited assistance in using the toilet, and supervision in eating; weight, 66 kg (145 lb); height, 170.3 cm (5' 8"); and has not had a recent decline in mood.

To calculate absolute lymphocyte count, multiply white blood cell count by percentage of lymphocytes: (8000/µL) × .15 = 1200/µL. To convert height to meters, recall that there are 2.54 cm per inch. Therefore, body mass index equals 66 divided by (68 × .0254)2 = 22.1.

The Missouri LRI Project risk score is calculated as follows:
(1 point for serum urea nitrogen) + (0 points for white blood cell count) + (0 points for absolute lymphocyte count <800/µL) + (1 point for pulse) + (1 point for sex) + (1 point for activities of daily living) + (2 points for body mass index) + (0 points for mood change) = 6 total points.

Table 7 shows that individuals with a score between 5 and 6 have a predicted 30-day mortality risk of 6.9%. Alternatively, using the logistic model in Table 4, a 6.7% mortality risk would be obtained as follows:
sum = (−4.53 + [0.046 × 20] + [0.052 × 8] + [0.613 × 0] + [0.017 × 80] + [0.555 × 1] + [0.31 × 2] − [0.089 × 22.1] + [0.97 × 0]) = −2.63
predicted mortality = esum/(1 + esum) = 0.067 or 6.7%

Table Graphic Jump LocationTable 6. Scoring System for Estimating 30-Day Mortality From Lower Respiratory Tract Infection*
Table Graphic Jump LocationTable 7. Observed and Predicted Mortality From Lower Respiratory Tract Infection Associated With Level of Risk Score*

We developed a new risk-prediction model for LRI in nursing home residents. In a large sample, our simplified scoring system identified 52% of residents with a low (score of 0-4) or relatively low (score of 5-6) 30-day mortality risk. Although many of these residents are likely candidates for nursing home management, 30% of those hospitalized within 48 hours in our study had scores of 0 to 6. Hospitalization rates for nursing home residents with infection and other acute illnesses vary substantially among nursing homes,3033 and some of such hospitalizations may be unnecessary.34 If confirmed in other settings, our model could be helpful in assessing the need for hospitalization. For higher-risk residents, decisions about treatment location will depend on individualized treatment goals and weighing the hazards of hospitalization against the nursing home's capability to provide adequate care.

For patients with community-acquired pneumonia, the current standard for estimating risk is the PSI.14 It uses age, sex, nursing home residence, altered mental status, vital signs, serum urea nitrogen, glucose, pH, serum sodium, oxygen saturation, presence of pleural effusion, and selected comorbid diseases to classify individuals into 5 risk groups. However, its structure (adding points for each year of age and for nursing home residence) places most nursing home residents in high-risk categories. In a retrospective study, the PSI predicted mortality reasonably well in 158 episodes of nursing home–acquired pneumonia9; however, 85% were classified in the highest-risk categories (classes IV and V). Although we studied the broader category of LRI and not just pneumonia, the PSI classifies 87% of our subjects in risk classes IV and V. While the PSI remains an important tool in the more general context for which it was developed, our model better distinguishes lower-risk episodes of LRI in the nursing home setting.

Our predictors bear some similarities but also notable differences to those in the PSI. Common variables to both predictive models include pulse, serum urea nitrogen, and male sex. Age, a key determinant of the PSI, dropped out early in our modeling process and is not statistically significant if added to our final model. This likely reflects the old age of our sample and the nursing home population in general. Among such individuals, functional measures, such as ADL status, provide more useful prognostic information than chronological age.

As with our model, ADL dependency has been repeatedly associated with LRI or pneumonia mortality in nursing home samples.57,10,11,35 Poor nutritional status has been linked to a variety of poor outcomes.36 Low BMI and low total lymphocyte count,37 which are 2 markers of poor nutritional status, were strongly associated with mortality from LRI in our model. It is possible that other nutritional variables might be superior, but both of these are readily available.

Our final 2 risk factors were elevated white blood cell count and decline in mood over the previous 90 days. While elevated white blood cell count has not been a significant predictor in previous multivariable models of mortality from pneumonia in nursing home settings, it was in 2 hospital-based studies of pneumonia outcomes38,39; one of these included just elderly patients.39 Major depression40 and comorbid depression4143 have been associated with mortality in nursing home residents, but not specifically from LRI. In our study, mood decline was a better predictor than summary depression scores, so it is not clear if this reflects depression or is a marker for general decline.

Several variables do not appear in our model. Rapid respiratory rate predicts mortality not only in the PSI, but also in 3 previous nursing home studies using multivariable analyses, including our pilot study.5,7,35 In the current study, pulse rate was highly correlated with respiratory rate and was a better predictor than respiratory rate. Nonetheless, absence of respiratory rate as a variable is a potential weakness of our risk prediction score, and it will be an important variable to assess in future studies evaluating our prediction rule. Oxygen saturation is also an important variable in the PSI, but such measurements were relatively uncommon in nursing homes during our study. For the 27% of subjects who had such data, adding oxygen saturation did not improve our prediction rule. Oxygen saturation data may play an important role in assessing treatment decisions in the future. Although fever, low temperature, and bilateral infiltrates have clinical appeal, and on a bivariable level are strongly related to mortality, they did not improve our model. In fact, low temperature and abnormal radiographic findings were included in our penultimate model (the 11-variable model) but were removed because they weakened the model in other random samples. Finally, several comorbid conditions, such as congestive heart failure, are important indicators in the PSI. None were significant in our multivariable modeling. Their lack of importance in our models may reflect their high prevalence and the high degree of disability among nursing home residents (Table 1).

Limitations

Our findings are subject to several limitations. A key issue is generalizability to other settings. All study facilities were in central or eastern Missouri. While they were similar in size and ownership to facilities nationally,15 factors affecting mortality could differ in other states or countries. More importantly, predictive models often perform less well in independently derived samples. Internal validation samples help avoid overfitting models to the peculiarities of a particular data set, but that is not sufficient to determine the ultimate utility of a prediction rule. Our model and its associated scoring system should be validated in other studies of nursing home residents to confirm their usefulness.

Second, important data may have been missing or misclassified. Although we identified subjects prospectively, some examination information had to be obtained from hospital records in 9.2% of evaluations. Hospital record data may not have been as detailed as project nurse assessments. Further, though all project nurses had strong assessment skills and additional training for this project, they might have missed some important findings. However, among variables ultimately included in our model, biases are unlikely. Most represented objective findings with high reliability, including pulse, sex, and the 3 laboratory results. Weight and height to compute BMI may be unreliable in nursing home records, but it is unlikely that they would be systematically biased across the study. Patient ADL status and information on mood decline were obtained from interviews with nursing home staff familiar with the resident.

Finally, we combined pneumonia and other LRIs in our analysis because clinically distinguishing between the 2 is often difficult, particularly in the nursing home setting, where physicians, advanced-practice nurses, or physician assistants are frequently unavailable to assess acutely ill residents. Portable radiographs obtained in nursing homes are of variable quality and require cautious interpretation. Although we made special efforts to ensure consistency in classifying radiology reports as possible, probable, or negative for pneumonia, we reviewed reports rather than radiographs in most cases. We may have misclassified some subjects as to whether their radiograph suggested pneumonia. We chose to review reports since reports and not radiographs are usually available to physicians caring for nursing home residents. Furthermore, because of our broader definition of LRI, a chest radiograph positive for pneumonia was not essential for study inclusion. However, pneumonia on chest radiograph was not a significant predictor of mortality in our multivariable model. These choices were intended to make our findings optimally useful to physicians making treatment decisions for ill nursing home residents with LRIs.

Conclusion

We identified a new predictive model for 30-day mortality risk in nursing home residents with LRIs. Our results are notable for identifying relatively low-risk residents. Our prediction rule could aid clinicians and researchers in optimizing care for nursing home residents with LRIs. As with all prediction rules, it should be validated in other settings.

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Morris JN, Hawes C, Fries BE.  et al.  Designing the national resident assessment instrument for nursing homes.  Gerontologist.1990;30:293-307.
 Long-Term Care Facility Resident Assessment Instrument (RAI) User's Manual: for Use With Version 2.0 of the Health Care Financing Administration's Minimum Data Set, Resident Assessment Protocols, and Utilization Guidelines.  Washington, DC: Health Care Financing Administration; 1995.
Hawes C, Morris JN, Phillips CD, Mor V, Fries BE, Nonemaker S. Reliability estimates for the Minimum Data Set for nursing home resident assessment and care screening (MDS).  Gerontologist.1995;35:172-178.
Morris JN, Fries BE, Mehr DR.  et al.  MDS cognitive performance scale.  J Gerontol.1994;49:M174-M182.
Morris JN, Fries BE, Morris SA. Scaling ADL's within the MDS.  J Gerontol A Biol Sci Med Sci.1999;54:M546-M553.
Harrell Jr FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med.1996;15:361-387.
 S-Plus 2000.  Seattle, Wash: Mathsoft, Inc, Data Analysis Products Division; 1999.
D'Agostino RB, Belanger AJ, Markson EW, Kelly-Hayes M, Wolf PA. Development of health risk appraisal functions in the presence of multiple indicators: the Framingham Study nursing home institutionalization model.  Stat Med.1995;14:1757-1770.
Preisser JS, Koch GG. Categorical data analysis in public health.  Annu Rev Public Health.1997;18:51-82.
 SAS System for Windows.  Version 6.1. Cary, NC: SAS Institute Inc; 1996.
D'Agostino Sr RB, Griffith JL, Schmid CH, Terrin N. Measures for evaluating model performance. In: Proceedings of the Biometrics Section, 1997. Alexandria, Va: American Statistical Association, Biometrics Section; 1998:253-258.
Hosmer Jr DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 1989.
Kayser-Jones JS, Wiener CL, Barbaccia JC. Factors contributing to the hospitalization of nursing home residents.  Gerontologist.1989;29:502-510.
Teresi JA, Holmes D, Bloom HG, Monaco C, Rosen S. Factors differentiating hospital transfers from long-term care facilities with high and low transfer rates.  Gerontologist.1991;31:795-806.
Scott HD, Logan M, Waters Jr WJ.  et al.  Medical practice variation in the management of acute medical events in nursing homes: a pilot study.  R I Med J.1988;71:69-74.
Smith WR, Kellerman A, Brown JS. The impact of nursing home transfer policies at the end of life on a public acute care hospital.  J Am Geriatr Soc.1995;43:1052-1057.
Saliba D, Kington R, Buchanan J.  et al.  Appropriateness of the decision to transfer nursing facility residents to the hospital.  J Am Geriatr Soc.2000;48:154-163.
Fried TR, Gillick MR, Lipsitz LA. Whether to transfer? factors associated with hospitalization and outcome of elderly long-term care patients with pneumonia.  J Gen Intern Med.1995;10:246-250.
Morley JE, Silver AJ. Nutritional issues in nursing home care.  Ann Intern Med.1995;123:850-859.
Singh MF, Rosenberg I. Nutrition and aging. In: Hazzard WR, Blass JP, Ettinger WH Jr, Holter JB, Ouslander JG, eds. Principles of Geriatric Medicine and Gerontology. New York, NY: McGraw-Hill; 1999:81-96.
British Thoracic Society and Public Health Laboratory Service.  Community-acquired pneumonia in adults in British hospitals in 1982-1983: a survey of aetiology, mortality, prognostic factors and outcome.  Q J Med.1987;62:195-220.
Zweig S, Lawhorne L, Post R. Factors predicting mortality in rural elderly hospitalized for pneumonia.  J Fam Pract.1990;30:153-159.
Rovner BW, German PS, Brant LJ, Clark R, Burton L, Folstein MF. Depression and mortality in nursing homes.  JAMA.1991;265:993-996.
Covinsky KE, Kahana E, Chin MH, Palmer RM, Fortinsky RH, Landefeld CS. Depressive symptoms and 3-year mortality in older hospitalized medical patients.  Ann Intern Med.1999;130:563-569.
Arfken CL, Lichtenberg PA, Tancer ME. Cognitive impairment and depression predict mortality in medically ill older adults.  J Gerontol A Biol Sci Med Sci.1999;54:M152-M156.
Cole MG, Bellavance F. Depression in elderly medical inpatients: a meta-analysis of outcomes.  CMAJ.1997;157:1055-1060.

Figures

Figure. Exclusion Criteria and Pathway to Study Sample
Graphic Jump Location
LRI indicates lower respiratory tract infection. Asterisk indicates some episodes were excluded for more than 1 reason so the sum exceeds the total number shown.

Tables

Table Graphic Jump LocationTable 1. Characteristics of Nursing Home Residents With 1406 Episodes of Lower Respiratory Tract Infections*
Table Graphic Jump LocationTable 2. Bivariable Relationship of Selected Signs and Symptoms With 30-Day Mortality in the Entire Data Set*
Table Graphic Jump LocationTable 3. Bivariable Relationship of Conditions With 30-Day Mortality in the Entire Data Set*
Table Graphic Jump LocationTable 4. Predictors of 30-Day Mortality in Residents With 4 or Fewer Episodes: Generalized Estimating Equations Analysis*
Table Graphic Jump LocationTable 5. Measures of Calibration and Discrimination for Generalized Estimating Equations Analysis of 30-Day Mortality in Development and Validation Data Sets and All Data Combined*
Table Graphic Jump LocationTable 6. Scoring System for Estimating 30-Day Mortality From Lower Respiratory Tract Infection*
Table Graphic Jump LocationTable 7. Observed and Predicted Mortality From Lower Respiratory Tract Infection Associated With Level of Risk Score*

References

Irvine PW, Van Buren N, Crossley K. Causes for hospitalization of nursing home residents: the role of infection.  J Am Geriatr Soc.1984;32:103-107.
Murtaugh CM, Freiman MP. Nursing home residents at risk of hospitalization and the characteristics of their hospital stays.  Gerontologist.1995;35:35-43.
Jackson MM, Fierer J, Barrett-Connor E.  et al.  Intensive surveillance for infections in a three-year study of nursing home patients.  Am J Epidemiol.1992;135:685-696.
Brooks S, Warshaw G, Hasse L, Kues JR. The physician decision-making process in transferring nursing home patients to the hospital.  Arch Intern Med.1994;154:902-908.
Degelau J, Guay D, Straub K, Luxenberg MG. Effectiveness of oral antibiotic treatment in nursing home-acquired pneumonia.  J Am Geriatr Soc.1995;43:245-251.
Muder RR, Brennen C, Swenson DL, Wagener M. Pneumonia in a long-term care facility: a prospective study of outcome.  Arch Intern Med.1996;156:2365-2370.
Mehr DR, Zweig SC, Kruse RL.  et al.  Mortality from lower respiratory infection in nursing home residents: a pilot prospective community-based study.  J Fam Pract.1998;47:298-304.
Houston MS, Silverstein MD, Suman VJ. Risk factors for 30-day mortality in elderly patients with lower respiratory tract infection: a community-based study.  Arch Intern Med.1997;157:2190-2195.
Mylotte JM, Naughton B, Saludades C, Maszarovics Z. Validation and application of the pneumonia prognosis index to nursing home residents with pneumonia.  J Am Geriatr Soc.1998;46:1538-1544.
Mehr DR, Foxman B, Colombo P. Risk factors for mortality from lower respiratory infections in nursing home patients.  J Fam Pract.1992;34:585-591.
Medina-Walpole AM, McCormick WC. Provider practice patterns in nursing home-acquired pneumonia.  J Am Geriatr Soc.1998;46:187-192.
Creditor MC. Hazards of hospitalization of the elderly.  Ann Intern Med.1993;118:219-223.
Rubenstein LZ, Ouslander JG, Wieland D. Dynamics and clinical implications of the nursing home-hospital interface.  Clin Geriatr Med.1988;4:471-491.
Fine MJ, Auble TE, Yealy DM.  et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia.  N Engl J Med.1997;336:243-250.
Gabrel CS, Jones A. The National Nursing Home Survey: 1997 summary.  Vital Health Stat 13.2000;:1-121.
McGeer A, Campbell B, Emori TG.  et al.  Definitions of infection for surveillance in long-term care facilities.  Am J Infect Control.1991;19:1-7.
Mehr DR, Binder EF, Kruse RL, Zweig SC, Madsen R, D'Agostino RB. Clinical findings associated with radiographic pneumonia in nursing home residents.  J Fam Pract.2001;50:931-937.
Morris JN, Hawes C, Fries BE.  et al.  Designing the national resident assessment instrument for nursing homes.  Gerontologist.1990;30:293-307.
 Long-Term Care Facility Resident Assessment Instrument (RAI) User's Manual: for Use With Version 2.0 of the Health Care Financing Administration's Minimum Data Set, Resident Assessment Protocols, and Utilization Guidelines.  Washington, DC: Health Care Financing Administration; 1995.
Hawes C, Morris JN, Phillips CD, Mor V, Fries BE, Nonemaker S. Reliability estimates for the Minimum Data Set for nursing home resident assessment and care screening (MDS).  Gerontologist.1995;35:172-178.
Morris JN, Fries BE, Mehr DR.  et al.  MDS cognitive performance scale.  J Gerontol.1994;49:M174-M182.
Morris JN, Fries BE, Morris SA. Scaling ADL's within the MDS.  J Gerontol A Biol Sci Med Sci.1999;54:M546-M553.
Harrell Jr FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med.1996;15:361-387.
 S-Plus 2000.  Seattle, Wash: Mathsoft, Inc, Data Analysis Products Division; 1999.
D'Agostino RB, Belanger AJ, Markson EW, Kelly-Hayes M, Wolf PA. Development of health risk appraisal functions in the presence of multiple indicators: the Framingham Study nursing home institutionalization model.  Stat Med.1995;14:1757-1770.
Preisser JS, Koch GG. Categorical data analysis in public health.  Annu Rev Public Health.1997;18:51-82.
 SAS System for Windows.  Version 6.1. Cary, NC: SAS Institute Inc; 1996.
D'Agostino Sr RB, Griffith JL, Schmid CH, Terrin N. Measures for evaluating model performance. In: Proceedings of the Biometrics Section, 1997. Alexandria, Va: American Statistical Association, Biometrics Section; 1998:253-258.
Hosmer Jr DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 1989.
Kayser-Jones JS, Wiener CL, Barbaccia JC. Factors contributing to the hospitalization of nursing home residents.  Gerontologist.1989;29:502-510.
Teresi JA, Holmes D, Bloom HG, Monaco C, Rosen S. Factors differentiating hospital transfers from long-term care facilities with high and low transfer rates.  Gerontologist.1991;31:795-806.
Scott HD, Logan M, Waters Jr WJ.  et al.  Medical practice variation in the management of acute medical events in nursing homes: a pilot study.  R I Med J.1988;71:69-74.
Smith WR, Kellerman A, Brown JS. The impact of nursing home transfer policies at the end of life on a public acute care hospital.  J Am Geriatr Soc.1995;43:1052-1057.
Saliba D, Kington R, Buchanan J.  et al.  Appropriateness of the decision to transfer nursing facility residents to the hospital.  J Am Geriatr Soc.2000;48:154-163.
Fried TR, Gillick MR, Lipsitz LA. Whether to transfer? factors associated with hospitalization and outcome of elderly long-term care patients with pneumonia.  J Gen Intern Med.1995;10:246-250.
Morley JE, Silver AJ. Nutritional issues in nursing home care.  Ann Intern Med.1995;123:850-859.
Singh MF, Rosenberg I. Nutrition and aging. In: Hazzard WR, Blass JP, Ettinger WH Jr, Holter JB, Ouslander JG, eds. Principles of Geriatric Medicine and Gerontology. New York, NY: McGraw-Hill; 1999:81-96.
British Thoracic Society and Public Health Laboratory Service.  Community-acquired pneumonia in adults in British hospitals in 1982-1983: a survey of aetiology, mortality, prognostic factors and outcome.  Q J Med.1987;62:195-220.
Zweig S, Lawhorne L, Post R. Factors predicting mortality in rural elderly hospitalized for pneumonia.  J Fam Pract.1990;30:153-159.
Rovner BW, German PS, Brant LJ, Clark R, Burton L, Folstein MF. Depression and mortality in nursing homes.  JAMA.1991;265:993-996.
Covinsky KE, Kahana E, Chin MH, Palmer RM, Fortinsky RH, Landefeld CS. Depressive symptoms and 3-year mortality in older hospitalized medical patients.  Ann Intern Med.1999;130:563-569.
Arfken CL, Lichtenberg PA, Tancer ME. Cognitive impairment and depression predict mortality in medically ill older adults.  J Gerontol A Biol Sci Med Sci.1999;54:M152-M156.
Cole MG, Bellavance F. Depression in elderly medical inpatients: a meta-analysis of outcomes.  CMAJ.1997;157:1055-1060.
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