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Derivation and Validation of a Clinical Prediction Score to Identify the Isolation of Pseudomonas in Pneumonia.

ABSTRACT: Given the focus of existing clinical prediction scores on identifying drug-resistant pathogens as a whole, the application to individual pathogens and other institutions may yield weaker performance. This study aimed to develop a locally derived clinical prediction model for Pseudomonas-mediated pneumonia. This retrospective study included patients ≥18 years of age who were admitted to an academic medical center between 1 July 2010 and 31 July 2020 with a CDC National Healthcare Safety Network confirmed pneumonia diagnosis and were receiving antimicrobials during the index encounter, with a positive respiratory culture. Cystic fibrosis patients were excluded. Logistic regression analysis identified risk factors associated with the isolation of Pseudomonas aeruginosa from respiratory cultures within the derivation cohort (n = 186), which were weighted to generate a prediction score that was applied to the derivation and internal validation (n = 95) cohorts. A total of 281 patients met the inclusion criteria. Five predictor variables were identified, namely, tracheostomy status (4 points), chronic obstructive pulmonary disease (5 points), enteral nutrition (9 points), chronic steroid use (11 points), and Pseudomonas aeruginosa isolation from any culture in the prior 6 months (14 points). At a score of >11, the prediction score demonstrated a sensitivity of 52.4% (95% confidence interval [CI], 36.4 to 68.0%) and a specificity of 84.9% (95% CI, 72.4 to 93.35%) in the validation cohort. Score accuracy was 70.5% (95% CI, 60.3 to 79.4%), and the area under the receiver operating characteristic curve (AUROC) was 0.77 (95% CI, 0.68 to 0.87) in the validation cohort. A prediction score for identifying Pseudomonas aeruginosa in pneumonia was derived, which may have the potential to decrease the use of broad-spectrum antibiotics. Validation with larger and external cohorts is necessary. IMPORTANCE In this study, we aimed to develop a locally derived clinical prediction model for Pseudomonas-mediated pneumonia. Utilizing a locally validated prediction score may help direct therapeutic management and be generalizable to other clinical settings and similar populations for the selection of appropriate antimicrobial coverage when data are lacking. Our study highlights a unique patient population, including immunocompromised, structural lung disease, and transplant patients. Five predictor variables were identified, namely, tracheostomy status, chronic obstructive pulmonary disease, enteral nutrition, chronic steroid use, and Pseudomonas aeruginosa isolation from any culture in the prior 6 months. A prediction score for identifying Pseudomonas aeruginosa in pneumonia was derived, which may have the potential to decrease the use of broad-spectrum antibiotics, although validation with larger and external cohorts is necessary.

SUBMITTER: Maskov Y

PROVIDER: S-EPMC9241902 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRespiratory viruses are important etiologies of community-acquired pneumonia. However, current knowledge on the prognosis of respiratory virus-related pneumonia (RV-p) is limited. Thus, here we aimed to establish a clinical predictive model for mortality of patients with RV-p.MethodsA total of 1431 laboratory-confirmed patients with RV-p, including 1169 and 262 patients from respective derivation and validation cohorts from five teaching hospitals in China were assessed between January 2010 and December 2019. A prediction rule was established on the basis of risk factors for 30-day mortality of patients with RV-p from the derivation cohort using a multivariate logistic regression model.ResultsThe 30-day mortality of patients with RV-p was 16.8% (241/1431). The RV-p score was composed of nine predictors (including respective points of mortality risk): (a) age ⩾65 years (1 point); (b) chronic obstructive pulmonary disease (1 point); (c) mental confusion (1 point); (d) blood urea nitrogen (1 point); (e) cardiovascular disease (2 points); (f) smoking history (2 points); (g) arterial pressure of oxygen/fraction of inspiration oxygen (PaO2/FiO2) < 250 mmHg (2 points); (h) lymphocyte counts <0.8 × 109/L (2 points); (i) arterial PH < 7.35 (3 points). A total of six points was used as the cut-off value for mortality risk stratification. Our model showed a sensitivity of 0.831 and a specificity of 0.783. The area under the receiver operating characteristic curve was more prominent for RV-p scoring [0.867, 95% confidence interval (CI)0.846-0.886] when compared with both pneumonia severity index risk (0.595, 95% CI 0.566-0.624, p < 0.001) and CURB-65 scoring (0.739, 95% CI 0.713-0.765, p < 0.001).ConclusionRV-p scoring was able to provide a good predictive accuracy for 30-day mortality, which accounted for a more effective stratification of patients with RV-p into relevant risk categories and, consequently, help physicians to make more rational clinical decisions.The reviews of this paper are available via the supplemental material section.

Project description:Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.

Project description:ObjectiveTo derive and validate an objective clinical prediction rule for the presence of uncomplicated ureteral stones in patients eligible for computed tomography (CT). We hypothesized that patients with a high probability of ureteral stones would have a low probability of acutely important alternative findings.DesignRetrospective observational derivation cohort; prospective observational validation cohort.SettingUrban tertiary care emergency department and suburban freestanding community emergency department.ParticipantsAdults undergoing non-contrast CT for suspected uncomplicated kidney stone. The derivation cohort comprised a random selection of patients undergoing CT between April 2005 and November 2010 (1040 patients); the validation cohort included consecutive prospectively enrolled patients from May 2011 to January 2013 (491 patients).Main outcome measuresIn the derivation phase a priori factors potentially related to symptomatic ureteral stone were derived from the medical record blinded to the dictated CT report, which was separately categorized by diagnosis. Multivariate logistic regression was used to determine the top five factors associated with ureteral stone and these were assigned integer points to create a scoring system that was stratified into low, moderate, and high probability of ureteral stone. In the prospective phase this score was observationally derived blinded to CT results and compared with the prevalence of ureteral stone and important alternative causes of symptoms.ResultsThe derivation sample included 1040 records, with five factors found to be most predictive of ureteral stone: male sex, short duration of pain, non-black race, presence of nausea or vomiting, and microscopic hematuria, yielding a score of 0-13 (the STONE score). Prospective validation was performed on 491 participants. In the derivation and validation cohorts ureteral stone was present in, respectively, 8.3% and 9.2% of the low probability (score 0-5) group, 51.6% and 51.3% of the moderate probability (score 6-9) group, and 89.6% and 88.6% of the high probability (score 10-13) group. In the high score group, acutely important alternative findings were present in 0.3% of the derivation cohort and 1.6% of the validation cohort.ConclusionsThe STONE score reliably predicts the presence of uncomplicated ureteral stone and lower likelihood of acutely important alternative findings. Incorporation in future investigations may help to limit exposure to radiation and over-utilization of imaging.Trial registrationwww.clinicaltrials.gov NCT01352676.

Project description:ImportanceA scoring tool to identify which adults with traumatic injury will require early laparotomy could help improve prehospital triage and system readiness.ObjectiveTo develop and validate a prediction model using prehospital information for early laparotomy following trauma.Design, setting, and participantsThis retrospective cohort study analyzed data from the 2017 version of the American College of Surgeons Trauma Quality Improvement Program database. All adult patients with traumatic injury aged 18 years or older who were admitted to a US trauma center in 2017 were included. Patients were randomly assigned to a derivation or validation cohort. Data were collected and analyzed between July 2020 and September 2020.Main outcomes and measuresThe primary outcome was laparotomy within 2 hours of hospital arrival. A scoring system was developed to predict early laparotomy using a logistic regression model in the derivation cohort. This was validated in the validation cohort using the area under the receiver operating characteristic curve.ResultsA total of 379 890 US adults with traumatic injury were included; 190 264 patients were in the derivation cohort and 189 626 patients were in the validation cohort. The cohorts had the same proportion of laparotomy within 2 hours of hospital arrival (1.1%). The median (IQR) age was 32 (25-46) years in the early laparotomy group and 54 (33-72) years in the group with no early laparotomy. The early laparotomy group contained 113 776 of 188 211 (60.5%) male patients, while the group with no early laparotomy contained 1702 of 2053 (82.9%) male patients. The variable most strongly associated with early laparotomy was penetrating injury to the head, neck, torso, or extremities proximal to the elbow or knee (odds ratio, 13.47; 95% CI, 12.22-14.86) with a point value of 10 (maximum overall score 20). Other variables included in the scoring system were the male sex, a systolic blood pressure less than 90 mm Hg, a Glasgow Coma Scale of less than or equal to 13, having chest wall instability or deformity, pelvic fracture, and high-risk blunt mechanism. In the validation cohort, the C statistic of the scoring system was 0.78 (95% CI, 0.77-0.79).Conclusions and relevanceIn this study, a novel scoring tool using prehospital information was derived and validated to identify which adults with traumatic injury will require laparotomy within 2 hours of hospital arrival. This tool may help trauma professionals allocate operative team resources before patient arrival.

Project description:ObjectivesFalls are a common problem in older people. Postural hypotension contributes to falls but is often asymptomatic. In the absence of symptoms, postural hypotension is only infrequently checked for in clinical practice. We undertook this study to derive, validate and explore the prospective associations of a prediction tool to identify people likely to have unrecognised postural hypotension.Design and settingCross-sectional and prospective multivariable cohort analysis.Participants1317 participants of the Invecchiare in Chianti study, a population-based cohort representative of the older Italian population.Primary outcome measuresPredictive value of score to suggest presence of postural hypotension.MethodsSubjects were randomised 1:1 to derivation or validation cohorts. Within the derivation cohort, univariable associations for candidate predictors of postural hypotension were tested. Variables with p<0.1 entered multivariable linear regression models. Factors retaining multivariable significance were incorporated into unweighted and weighted Detecting Risk Of Postural hypotension (DROP) scores. These scores were tested in the validation cohort against prediction of postural hypotension, cognitive decline and mortality over 9 years of follow-up.ResultsPostural hypotension was present in 203 (15.4%) of participants. Factors predicting postural hypotension were: digoxin use, Parkinson's disease, hypertension, stroke or cardiovascular disease and an interarm systolic blood pressure difference. Area under the curve was consistent at 0.65 for all models, with significant ORs of 1.8 to 2.4 per unit increase in score for predicting postural hypotension. For a DROP score ?1, five cases need to be tested to identify one with postural hypotension.Increasing DROP scores predicted mortality (OR 1.8 to 2.8 per unit rise) and increasing rates of decline of Mini Mental State Examination score (analysis of variance p<0.001) over 9 years of follow-up.ConclusionsThe DROP score provides a simple method to identify people likely to have postural hypotension and increased risks to health who require further evaluation.

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Derivation and Validation of a Clinical Prediction Score to Identify the Isolation of Pseudomonas in Pneumonia.

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