A prediction rule to identify low-risk patients with community-acquired pneumonia
23 Jan 1997-The New England Journal of Medicine (Massachusetts Medical Society)-Vol. 336, Iss: 4, pp 243-250
TL;DR: A prediction rule that stratifies patients into five classes with respect to the risk of death within 30 days accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes and may help physicians make more rational decisions about hospitalization for patients with pneumonia.
Abstract: Background There is considerable variability in rates of hospitalization of patients with community-acquired pneumonia, in part because of physicians' uncertainty in assessing the severity of illness at presentation. Methods From our analysis of data on 14,199 adult inpatients with community-acquired pneumonia, we derived a prediction rule that stratifies patients into five classes with respect to the risk of death within 30 days. The rule was validated with 1991 data on 38,039 inpatients and with data on 2287 inpatients and outpatients in the Pneumonia Patient Outcomes Research Team (PORT) cohort study. The prediction rule assigns points based on age and the presence of coexisting disease, abnormal physical findings (such as a respiratory rate of > or = 30 or a temperature of > or = 40 degrees C), and abnormal laboratory findings (such as a pH or = 30 mg per deciliter [11 mmol per liter] or a sodium concentration Results There were no significant differences in mortality in each of the five risk classes among the three cohorts. Mortality ranged 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 0.9 to 2.8 percent for class III (P=0.12). Among the 1575 patients in 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 of subsequent hospitalization among those treated as outpatients and with the use of intensive care and the number of days in the hospital among inpatients. Conclusions The prediction rule we describe accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes. This prediction rule may help physicians make more rational decisions about hospitalization for patients with pneumonia.
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TL;DR: Although high fever was associated with the development of ARDS, it was also associated with better outcomes among patients with ARDS and treatment with methylprednisolone may be beneficial for patients who develop ARDS.
Abstract: Importance Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. Risk factors for the clinical outcomes of COVID-19 pneumonia have not yet been well delineated. Objective To describe the clinical characteristics and outcomes in patients with COVID-19 pneumonia who developed acute respiratory distress syndrome (ARDS) or died. Design, Setting, and Participants Retrospective cohort study of 201 patients with confirmed COVID-19 pneumonia admitted to Wuhan Jinyintan Hospital in China between December 25, 2019, and January 26, 2020. The final date of follow-up was February 13, 2020. Exposures Confirmed COVID-19 pneumonia. Main Outcomes and Measures The development of ARDS and death. Epidemiological, demographic, clinical, laboratory, management, treatment, and outcome data were also collected and analyzed. Results Of 201 patients, the median age was 51 years (interquartile range, 43-60 years), and 128 (63.7%) patients were men. Eighty-four patients (41.8%) developed ARDS, and of those 84 patients, 44 (52.4%) died. In those who developed ARDS, compared with those who did not, more patients presented with dyspnea (50 of 84 [59.5%] patients and 30 of 117 [25.6%] patients, respectively [difference, 33.9%; 95% CI, 19.7%-48.1%]) and had comorbidities such as hypertension (23 of 84 [27.4%] patients and 16 of 117 [13.7%] patients, respectively [difference, 13.7%; 95% CI, 1.3%-26.1%]) and diabetes (16 of 84 [19.0%] patients and 6 of 117 [5.1%] patients, respectively [difference, 13.9%; 95% CI, 3.6%-24.2%]). In bivariate Cox regression analysis, risk factors associated with the development of ARDS and progression from ARDS to death included older age (hazard ratio [HR], 3.26; 95% CI 2.08-5.11; and HR, 6.17; 95% CI, 3.26-11.67, respectively), neutrophilia (HR, 1.14; 95% CI, 1.09-1.19; and HR, 1.08; 95% CI, 1.01-1.17, respectively), and organ and coagulation dysfunction (eg, higher lactate dehydrogenase [HR, 1.61; 95% CI, 1.44-1.79; and HR, 1.30; 95% CI, 1.11-1.52, respectively] and D-dimer [HR, 1.03; 95% CI, 1.01-1.04; and HR, 1.02; 95% CI, 1.01-1.04, respectively]). High fever (≥39 °C) was associated with higher likelihood of ARDS development (HR, 1.77; 95% CI, 1.11-2.84) and lower likelihood of death (HR, 0.41; 95% CI, 0.21-0.82). Among patients with ARDS, treatment with methylprednisolone decreased the risk of death (HR, 0.38; 95% CI, 0.20-0.72). Conclusions and Relevance Older age was associated with greater risk of development of ARDS and death likely owing to less rigorous immune response. Although high fever was associated with the development of ARDS, it was also associated with better outcomes among patients with ARDS. Moreover, treatment with methylprednisolone may be beneficial for patients who develop ARDS.
6,335 citations
McMaster University1, Northwestern University2, University of Texas Health Science Center at San Antonio3, Johns Hopkins University4, University of Mississippi5, University of Utah6, LDS Hospital7, Centers for Disease Control and Prevention8, Baylor College of Medicine9, United States Department of Veterans Affairs10, Winthrop-University Hospital11, Stony Brook University12, University of Barcelona13
TL;DR: This work presents a meta-analyses of the immune system’s response to chronic obstructive pulmonary disease and shows clear patterns of decline in the immune systems of elderly patients with compromised immune systems.
Abstract: Lionel A. Mandell, Richard G. Wunderink, Antonio Anzueto, John G. Bartlett, G. Douglas Campbell, Nathan C. Dean, Scott F. Dowell, Thomas M. File, Jr. Daniel M. Musher, Michael S. Niederman, Antonio Torres, and Cynthia G. Whitney McMaster University Medical School, Hamilton, Ontario, Canada; Northwestern University Feinberg School of Medicine, Chicago, Illinois; University of Texas Health Science Center and South Texas Veterans Health Care System, San Antonio, and Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas; Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi School of Medicine, Jackson; Division of Pulmonary and Critical Care Medicine, LDS Hospital, and University of Utah, Salt Lake City, Utah; Centers for Disease Control and Prevention, Atlanta, Georgia; Northeastern Ohio Universities College of Medicine, Rootstown, and Summa Health System, Akron, Ohio; State University of New York at Stony Brook, Stony Brook, and Department of Medicine, Winthrop University Hospital, Mineola, New York; and Cap de Servei de Pneumologia i Allergia Respiratoria, Institut Clinic del Torax, Hospital Clinic de Barcelona, Facultat de Medicina, Universitat de Barcelona, Institut d’Investigacions Biomediques August Pi i Sunyer, CIBER CB06/06/0028, Barcelona, Spain.
5,558 citations
Cites background or methods from "A prediction rule to identify low-r..."
...Also, the presence of rare illnesses, such as neuromuscular or sickle cell disease, may require hospitalization but not affect the PSI score....
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...Use of the PSI score in clinical trials has demonstrated some of its limitations, which may be equally applicable to other scoring techniques....
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...Atlas et al. [25] were able to reduce hospital admissions among patients in PSI risk classes I–III from 58% in a retrospective control group to 43% in a PSI-based intervention group....
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...Their guideline used the PSI for admission decision support and included recommendations for antibiotic therapy, timing of first antibiotic dose, measurement of oxygen saturation, and blood cultures for admitted patients....
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...Calculating the PSI score and assigning the risk class, providing oral clarithromycin, and home nursing follow-up significantly ( ) decreased the number of low-P p .01 mortality-risk admissions [25]....
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TL;DR: A simple six point score based on confusion, urea, respiratory rate, blood pressure, and age can be used to stratify patients with CAP into different management groups.
Abstract: Background: In the assessment of severity in community acquired pneumonia (CAP), the modified British Thoracic Society (mBTS) rule identifies patients with severe pneumonia but not patients who might be suitable for home management. A multicentre study was conducted to derive and validate a practical severity assessment model for stratifying adults hospitalised with CAP into different management groups. Methods: Data from three prospective studies of CAP conducted in the UK, New Zealand, and the Netherlands were combined. A derivation cohort comprising 80% of the data was used to develop the model. Prognostic variables were identified using multiple logistic regression with 30 day mortality as the outcome measure. The final model was tested against the validation cohort. Results: 1068 patients were studied (mean age 64 years, 51.5% male, 30 day mortality 9%). Age ⩾65 years (OR 3.5, 95% CI 1.6 to 8.0) and albumin C onfusion, U rea >7 mmol/l, R espiratory rate ⩾30/min, low systolic( B lood pressure), age ⩾65 years (CURB-65 score) based on information available at initial hospital assessment, enabled patients to be stratified according to increasing risk of mortality: score 0, 0.7%; score 1, 3.2%; score 2, 3%; score 3, 17%; score 4, 41.5% and score 5, 57%. The validation cohort confirmed a similar pattern. Conclusions: A simple six point score based on confusion, urea, respiratory rate, blood pressure, and age can be used to stratify patients with CAP into different management groups.
2,576 citations
TL;DR: This study highlights the need to understand more fully the role of Epstein-Barr virus in the development of central giant cell granuloma and its role in the immune system.
Abstract: John G. Bartlett,1 Scott F Dowell,2 Lionel A. Mandell,6 Thomas M. File, Jr.,3 Daniel M. Musher,4 and Michael J. Fine5 'Johns Hopkins University School of Medicine, Baltimore, Maryland, 2Centers for Disease Control and Prevention, Atlanta, Georgia, 3Northeastern Ohio Universities College of Medicine, Cleveland, Ohio, 4Baylor College of Medicine and Veterans Affairs Medical Center, Houston, Texas, and 5University of Pittsburgh, Pennsylvania, USA; and 6McMaster University, Toronto, Canada
2,292 citations
Cites background from "A prediction rule to identify low-r..."
...Numerous studies have identified risk factors for death in cases of CAP [9, 10, 12]....
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...Independent associations with increased mortality have also been demonstrated for a variety of comorbid illnesses, such as active malignancies [10, 16, 19], immunosuppression [20, 21], neurological disease [19, 22, 23], congestive heart failure [10, 17, 19], coronary artery disease [19], and diabetes mellitus [10, 19, 24]....
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...Mortality is estimated to be !1% for patients not hospitalized [9, 10]....
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...Laboratory and radiographic findings independently associated with increased mortality are hyponatremia [10, 19], hyperglycemia [10, 19], azotemia [10, 19, 27, 28], hypoalbuminemia [16, 19, 22, 25], hypoxemia [10, 19], liver function test abnormalities [19], and pleural effusion [29]....
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...Signs and symptoms independently associated with increased mortality consist of dyspnea [10], chills [25], altered mental status [10, 19, 23, 26], hypothermia or hyperthermia [10, 16, 17, 20], tachypnea [10, 19, 23, 27], and hypotension (diastolic and systolic) [10, 19, 26–28]....
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References
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TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Abstract: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect difference...
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31 Dec 1980
TL;DR: Statistical methods in cancer research as mentioned in this paper, Statistical Methods in Cancer Research, Statistical methods in Cancer research, Statistical methods for cancer research, کتابخانه مرکزی دانشگاه علوم پزش
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TL;DR: This paper discusses the design of clinical trials, use of computer software in survival analysis, and some non-parametric procedures for modelling survival data.
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