Classification algorithms to improve the accuracy of identifying patients hospitalized with community-acquired pneumonia using administrative data

2010 ◽  
Vol 139 (9) ◽  
pp. 1296-1306 ◽  
Author(s):  
O. YU ◽  
J. C. NELSON ◽  
L. BOUNDS ◽  
L. A. JACKSON
Keyword(s):  
Administrative Data ◽  
Regression Tree ◽  
Epidemiological Studies ◽  
Community Acquired Pneumonia ◽  
Classification And Regression Tree ◽  
Classification Algorithms ◽  
Care Organization ◽  
Discharge Diagnosis ◽  
Diagnosis Code ◽  
Predictive Values

SUMMARYIn epidemiological studies of community-acquired pneumonia (CAP) that utilize administrative data, cases are typically defined by the presence of a pneumonia hospital discharge diagnosis code. However, not all such hospitalizations represent true CAP cases. We identified 3991 hospitalizations during 1997–2005 in a managed care organization, and validated them as CAP or not by reviewing medical records. To improve the accuracy of CAP identification, classification algorithms that incorporated additional administrative information associated with the hospitalization were developed using the classification and regression tree analysis. We found that a pneumonia code designated as the primary discharge diagnosis and duration of hospital stay improved the classification of CAP hospitalizations. Compared to the commonly used method that is based on the presence of a primary discharge diagnosis code of pneumonia alone, these algorithms had higher sensitivity (81–98%) and positive predictive values (82–84%) with only modest decreases in specificity (48–82%) and negative predictive values (75–90%).

scholarly journals Validation of Coding Algorithms for the Identification of Patients with Primary Biliary Cirrhosis Using Administrative Data

2010 ◽  
Vol 24 (3) ◽  
pp. 175-182 ◽  
Author(s):  
Robert P Myers ◽  
Abdel Aziz M Shaheen ◽  
Andrew Fong ◽  
Alex F Wan ◽  
Mark G Swain ◽  
...  
Keyword(s):  
Primary Biliary Cirrhosis ◽  
Administrative Data ◽  
Large Scale ◽  
Optimal Algorithm ◽  
Epidemiological Studies ◽  
Population Based ◽  
Sensitivity Analyses ◽  
Administrative Databases ◽  
Biliary Cirrhosis ◽  
Predictive Values

BACKGROUND: Large-scale epidemiological studies of primary biliary cirrhosis (PBC) have been hindered by difficulties in case ascertainment.OBJECTIVE: To develop coding algorithms for identifying PBC patients using administrative data – a widely available data source.METHODS: Population-based administrative databases were used to identify patients with a diagnosis code for PBC from 1994 to 2002. Coding algorithms for confirmed PBC (two or more of antimitochondrial antibody positivity, cholestatic liver biochemistry and/or compatible liver histology) were derived using chart abstraction data as the reference. Patients with a recorded PBC diagnosis but insufficient confirmatory data were classified as ‘suspected PBC’.RESULTS: Of 189 potential PBC cases, 119 (60%) had confirmed PBC and 28 (14%) had suspected PBC. The optimal algorithm including two or more uses of a PBC code had a sensitivity of 94% (95% CI 71% to 100%) and positive predictive values of 73% (95% CI 61% to 75%) for confirmed PBC, and 89% (95% CI 82% to 94%) for confirmed or suspected PBC. Sensitivity analyses revealed greater accuracy among women, and with the use of multiple data sources and one or more years of data. Inclusion of diagnosis codes for conditions frequently misclassified as PBC did not improve algorithm performance.CONCLUSIONS: Administrative databases can reliably identify patients with PBC and may facilitate epidemiological investigations of this condition.

scholarly journals Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Catherine L. Satterwhite ◽  
Onchee Yu ◽  
Marsha A. Raebel ◽  
Stuart Berman ◽  
Penelope P. Howards ◽  
...  
Keyword(s):  
Pelvic Inflammatory Disease ◽  
Medical Record ◽  
Administrative Data ◽  
Inflammatory Disease ◽  
Regression Tree ◽  
Case Finding ◽  
Medical Record Review ◽  
Classification And Regression Tree ◽  
Case Identification ◽  
Record Review

ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15–44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15–25 years as predictors. Algorithm sensitivity (GH=96.4%;KPCO=90.3%) and PPV (GH=86.9%;KPCO=84.5%) were high, but specificity was poor (GH=45.9%;KPCO=37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.

scholarly journals Detection of Residents With Progress Issues Using a Keyword–Specific Algorithm

2019 ◽  
Vol 11 (6) ◽  
pp. 656-662 ◽  
Author(s):  
Gaby Tremblay ◽  
Pierre-Hugues Carmichael ◽  
Jean Maziade ◽  
Mireille Grégoire
Keyword(s):  
At Risk ◽  
Recursive Partitioning ◽  
Regression Tree ◽  
Poor Performance ◽  
Assessment System ◽  
Classification And Regression Tree ◽  
Predictive Values ◽  
Electronic Assessment ◽  
Classification And Regression ◽  
Difficulty Score

ABSTRACT Background The literature suggests that specific keywords included in summative rotation assessments might be an early indicator of abnormal progress or failure. Objective This study aims to determine the possible relationship between specific keywords on in-training evaluation reports (ITERs) and subsequent abnormal progress or failure. The goal is to create a functional algorithm to identify residents at risk of failure. Methods A database of all ITERs from all residents training in accredited programs at Université Laval between 2001 and 2013 was created. An instructional designer reviewed all ITERs and proposed terms associated with reinforcing and underperformance feedback. An algorithm based on these keywords was constructed by recursive partitioning using classification and regression tree methods. The developed algorithm was tuned to achieve 100% sensitivity while maximizing specificity. Results There were 41 618 ITERs for 3292 registered residents. Residents with failure to progress were detected for family medicine (6%, 67 of 1129) and 36 other specialties (4%, 78 of 2163), while the positive predictive values were 23.3% and 23.4%, respectively. The low positive predictive value may be a reflection of residents improving their performance after receiving feedback or a reluctance by supervisors to ascribe a “fail” or “in difficulty” score on the ITERs. Conclusions Classification and regression trees may be helpful to identify pertinent keywords and create an algorithm, which may be implemented in an electronic assessment system to detect future residents at risk of poor performance.

scholarly journals Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sarika K. L. Hogendoorn ◽  
Loïc Lhopitallier ◽  
Melissa Richard-Greenblatt ◽  
Estelle Tenisch ◽  
Zainab Mbarack ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Respiratory Rate ◽  
Lower Respiratory Tract ◽  
Predictive Accuracy ◽  
Clinical Signs ◽  
Regression Tree ◽  
Community Acquired Pneumonia ◽  
Classification And Regression Tree ◽  
Respiratory Pathogens ◽  
Tract Infections

Abstract Background Inappropriate antibiotics use in lower respiratory tract infections (LRTI) is a major contributor to resistance. We aimed to design an algorithm based on clinical signs and host biomarkers to identify bacterial community-acquired pneumonia (CAP) among patients with LRTI. Methods Participants with LRTI were selected in a prospective cohort of febrile (≥ 38 °C) adults presenting to outpatient clinics in Dar es Salaam. Participants underwent chest X-ray, multiplex PCR for respiratory pathogens, and measurements of 13 biomarkers. We evaluated the predictive accuracy of clinical signs and biomarkers using logistic regression and classification and regression tree analysis. Results Of 110 patients with LRTI, 17 had bacterial CAP. Procalcitonin (PCT), interleukin-6 (IL-6) and soluble triggering receptor expressed by myeloid cells-1 (sTREM-1) showed an excellent predictive accuracy to identify bacterial CAP (AUROC 0.88, 95%CI 0.78–0.98; 0.84, 0.72–0.99; 0.83, 0.74–0.92, respectively). Combining respiratory rate with PCT or IL-6 significantly improved the model compared to respiratory rate alone (p = 0.006, p = 0.033, respectively). An algorithm with respiratory rate (≥ 32/min) and PCT (≥ 0.25 μg/L) had 94% sensitivity and 82% specificity. Conclusions PCT, IL-6 and sTREM-1 had an excellent predictive accuracy in differentiating bacterial CAP from other LRTIs. An algorithm combining respiratory rate and PCT displayed even better performance in this sub-Sahara African setting.

scholarly journals Clinical Sign and Biomarker-based Algorithm to Identify Bacterial Pneumonia Among Outpatients with Lower Respiratory Tract Infection in Tanzania

2021 ◽  
Author(s):  
Sarika K.L. Hogendoorn ◽  
Loïc Lhopitallier ◽  
Melissa Richard-Greenblatt ◽  
Estelle Tenisch ◽  
Zainab Mbarack ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Respiratory Rate ◽  
Lower Respiratory Tract ◽  
Predictive Accuracy ◽  
Clinical Signs ◽  
Regression Tree ◽  
Community Acquired Pneumonia ◽  
Classification And Regression Tree ◽  
Respiratory Pathogens ◽  
Tract Infections

Abstract Background.Inappropriate antibiotics in lower respiratory tract infections (LRTI) is a major contributor to resistance. We aimed to design an algorithm based on clinical signs and host biomarkers to identify bacterial community-acquired pneumonia (CAP) among patients with LRTI.Methods. Participants with LRTI were selected in a prospective cohort of febrile (≥38°C) adults presenting to outpatient clinics in Dar es Salaam. Participants underwent chest X-ray, multiplex PCR for respiratory pathogens, and measurements of 13 biomarkers. We evaluated the predictive accuracy of clinical signs and biomarkers using logistic regression and classification and regression tree analysis.Results. Of 110 patients with LRTI, 17 had bacterial CAP. Procalcitonin (PCT), interleukin-6 (IL-6) and soluble triggering receptor expressed by myeloid cells-1 (sTREM-1) showed excellent predictive accuracy to identify bacterial CAP (AUROC 0.88, 95%CI 0.78-0.98; 0.84, 0.72-0.99; 0.83, 0.74-0.92, respectively). Combining respiratory rate with PCT or IL-6 significantly improved the model compared to respiratory rate alone (p=0.006, p=0.033, respectively). An algorithm with respiratory rate (≥32/minute) and PCT (≥0.25 μg/L) had 94% sensitivity and 82% specificity.Conclusions. PCT, IL-6 and sTREM-1 had excellent predictive accuracy in differentiating bacterial CAP from other LRTIs. An algorithm combining respiratory rate and PCT displayed even better performance in this sub-Sahara African setting.

GCS-Pupil Score Has a Stronger Association with Mortality and Poor Functional Outcome than GCS Alone in Pediatric Severe Traumatic Brain Injury

2021 ◽  
pp. 1-8
Author(s):  
Binod Balakrishnan ◽  
Heather VanDongen-Trimmer ◽  
Irene Kim ◽  
Sheila J. Hanson ◽  
Liyun Zhang ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Outcome ◽  
Cerebral Performance Category ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Continuous Variables ◽  
Poor Functional Outcome ◽  
Severity Scores ◽  
Pupil Reactivity

<b><i>Background:</i></b> The Glasgow Coma Scale (GCS), used to classify the severity of traumatic brain injury (TBI), is associated with mortality and functional outcomes. However, GCS can be affected by sedation and neuromuscular blockade. GCS-Pupil (GCS-P) score, calculated as GCS minus Pupil Reactivity Score (PRS), was shown to better predict outcomes in a retrospective cohort of adult TBI patients. We evaluated the applicability of GCS-P to a large retrospective pediatric severe TBI (sTBI) cohort. <b><i>Methods:</i></b> Admissions to pediatric intensive care units in the Virtual Pediatric Systems (VPS, LLC) database from 2010 to 2015 with sTBI were included. We collected GCS, PRS (number of nonreactive pupils), cardiac arrest, abusive head trauma status, illness severity scores, pediatric cerebral performance category (PCPC) score, and mortality. GCS-P was calculated as GCS minus PRS. χ<sup>2</sup> or Fisher’s exact test and Mann-Whitney U test compared categorical and continuous variables, respectively. Classification and regression tree analysis identified thresholds of GCS-P and GCS along with other independent factors which were further examined using multivariable regression analysis to identify factors independently associated with mortality and unfavorable PCPC at PICU discharge. <b><i>Results:</i></b> Among the 2,682 patients included in the study, mortality was 23%, increasing from 4.7% for PRS = 0 to 80% for PRS = 2. GCS-P identified more severely injured patients with GCS-P scores 1 and 2 who had worse outcomes. GCS-P ≤ 2 had higher odds for mortality, OR = 68.4 (95% CI = 50.6–92.4) and unfavorable PCPC, OR = 17.3 (8.1, 37.0) compared to GCS ≤ 5. GCS-P ≤ 2 also had higher specificity and positive predictive value for both mortality and unfavorable PCPC compared to GCS ≤ 5. <b><i>Conclusions:</i></b> GCS-P, by incorporating pupil reactivity to GCS scoring, is more strongly associated with mortality and poor functional outcome at PICU discharge in children with sTBI.

scholarly journals Edible Insects: Preliminary Study about Perceptions, Attitudes, and Knowledge on a Sample of Portuguese Citizens

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 709
Author(s):  
Sofia G. Florença ◽  
Paula M. R. Correia ◽  
Cristina A. Costa ◽  
Raquel P. F. Guiné
Keyword(s):  
Nutritive Value ◽  
Meat Products ◽  
Regression Tree ◽  
Good Alternative ◽  
Classification And Regression Tree ◽  
Categorical Variables ◽  
Final Conclusion ◽  
Edible Insects ◽  
Chi Square ◽  
Alternative Sources

This study investigated the knowledge, attitudes, consumption habits, and degree of acceptability of edible insects (EI) or derived products among Portuguese consumers. This work consisted of a questionnaire survey, undertaken on a sample of 213 participants. For the treatment of data, basic descriptive statistics were used, complemented with chi-square tests to assess some associations between categorical variables. Moreover, a tree classification analysis was carried out using a classification and regression tree (CRT) algorithm with cross-validation. The results indicated that people tend to have correct perceptions about the sustainability issues associated with the use of insects as alternative sources of protein; however, the level of knowledge and overall perception about their nutritive value is low. Regarding the consumption of EI, it was found that only a small part of the participants had already eaten them, doing it mostly abroad, by self-initiative, in a restaurant or at a party or event. Additionally, it was found that the reluctance to consume insects is higher if they are whole, but when they are transformed into ingredients used in food formulations, the level of acceptance increases. Furthermore, men have shown to have a better perception about EI, be more informed about sustainability, and have a higher level of acceptability when compared to women. As a final conclusion, it was observed that the Portuguese still show some resistance to adhere to the use of insects as replacements for meat products, but the market of insect based products can be a good alternative to overpass the neophobia associated with this type of food.

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