scholarly journals Comparison of Intensive Care Outcome Prediction Models Based on Admission Scores With those Based on 24-Hour Data

2008 ◽  
Vol 36 (6) ◽  
pp. 845-849 ◽  
Author(s):  
G. J. Duke ◽  
M. Piercy ◽  
D. Digiantomasso ◽  
J. V. Green
Keyword(s):  
Intensive Care ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Apache Ii ◽  
Standardised Mortality Ratio ◽  
Receiver Operating Characteristic Plot ◽  
Mortality Ratio ◽  
Receiver Operating

We compared the performance of six outcome prediction models - three based on 24-hour data and three based on admission-only data - in a metropolitan university-affiliated teaching hospital with a 10-bed intensive care unit. The Acute Physiology and Chronic Health Evaluation models, version II (APACHE II) and version III-J, and the Simplified Acute Physiology Score version II (SAPS II) are based on 24-hour data and were compared with the Mortality Prediction Model version II and the SAPS version III using international and Australian coefficients (SAPS IIIA). Data were collected prospectively according to the standard methodologies for each model. Calibration and discrimination for each model were assessed by the standardised mortality ratio, area under the receiver operating characteristic plot and Hosmer-Lemeshow contingency tables and chi-squared statistics (C10 and H10). Predetermined criteria were area under the receiver operating characteristic plot >0.8, standardised mortality ratio 95% confidence interval includes 1.0, and C10 and H10 P values >0.05. Between October 1, 2005 and December 31, 2007, 1843 consecutive admissions were screened and after the standard exclusions, 1741 were included in the analysis. The SAAPS II and SAPS IIIA models fulfilled and the APACHE II model failed all criteria. The other models satisfied the discrimination criterion but significantly over-predicted mortality risk and require recalibration. Outcome prediction models based on admission-only data compared favourably to those based on 24-hour data.

scholarly journals Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

2021 ◽  
Author(s):  
Victor Alfonso Rodriguez ◽  
Shreyas Bhave ◽  
Ruijun Chen ◽  
Chao Pang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
At Risk ◽  
Mechanical Ventilation ◽  
Renal Replacement Therapy ◽  
Receiver Operating Characteristic ◽  
Replacement Therapy ◽  
Operating Characteristic ◽  
Prediction Models ◽  
External Validation ◽  
Electronic Health Record Data ◽  
Receiver Operating

Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

scholarly journals External Validation of Five Scores to Predict Stroke-Associated Pneumonia and the Role of Selected Blood Biomarkers

Stroke ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 325-330
Author(s):  
Benjamin Hotter ◽  
Sarah Hoffmann ◽  
Lena Ulm ◽  
Christian Meisel ◽  
Alejandro Bustamante ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Literature Search ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Data Set ◽  
Biomarker Analysis ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and Purpose: Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores. Methods: We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models. Results: The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05. Conclusions: All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01264549 and NCT01079728.

scholarly journals Epidemiological and Clinical Predictors of COVID-19

2020 ◽  
Vol 71 (15) ◽  
pp. 786-792 ◽  
Author(s):  
Yinxiaohe Sun ◽  
Vanessa Koh ◽  
Kalisvar Marimuthu ◽  
Oon Tek Ng ◽  
Barnaby Young ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Prediction Models ◽  
Tertiary Care ◽  
Laboratory Data ◽  
Care Hospital ◽  
Laboratory Test Results ◽  
Leave One Out ◽  
Receiver Operating

Abstract Background Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid–based reverse transcription polymerase chain reaction (PCR) testing. Methods This retrospective case-control study involves subjects (7–98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs, or throat swabs. Demographic, clinical, laboratory, and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike’s information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation. Results The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years, and 407 (51.7%) were female. Using leave-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively. In comparison, model 4 had an AUC of 0.65. Conclusions Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR testing and containment efforts. Basic laboratory test results were crucial to prediction models.

scholarly journals Perbandingan Validitas Sistem Skoring Apache II, SOFA, Dan Customized Sequential Organ Failure Assessment (Csofa) Untuk Memperkirakan Mortalitas Pasien Non-Bedah Yang Dirawat Di Ruang Perawatan Intensif

2015 ◽  
Vol 7 (2) ◽  
pp. 102 ◽  
Author(s):  
Stefanus Taofik ◽  
Tjokorda Agung Senapathi ◽  
I Made Wiryana
Keyword(s):  
Sequential Organ Failure Assessment ◽  
Receiver Operating Characteristic ◽  
Organ Failure ◽  
Operating Characteristic ◽  
Apache Ii ◽  
Missing Value ◽  
Failure Assessment ◽  
Receiver Operating

Latar Belakang : Penerapan Sistem Jaminan Kesehatan Nasional (SJKN) dalam pelayanan ICU mendorong pelayanan ICU untuk lebih efektif dan efisien. Prediksi hasil perawatan penting baik secara administrasi ataupun klinis dalam manajemen ICU. Pasien non-bedah meskipun jumlahnya tidak banyak, namun memiliki angka mortalitas yang tinggi.Tujuan : Untuk mendapatkan sistem skoring yang baik dan mudah diterapkan dilakukan penilaian missing value, dan diskriminasi dari masing masing sistem skoring.Metode : Penelitian ini melibatkan 184 pasien non-bedah yang dirawat di ICU RSUP Sanglah Denpasar yang diambil secara retrospektif dari data tanggal 1 Januari 2014 sampai dengan 31 Desember 2014. Semua pasien dilakukan penilaian APACHE II, SOFA, dan CSOFA. Uji analisis regresi logistik dilakukan untuk menilai pengaruh masing masing sub variabel terhadap mortalitas, dan selanjutnya mencari cut off point dari analisis kurva ROC untuk mendapatkan sensitifitas dan spesifisitas masing masing.Hasil : Area under Receiver Operating Characteristic (AuROC) pada APACHE II, SOFA, dan CSOFA berturut turut didapatkan 0,892, 0,919, dan 0,9172. Missing value terbanyak didapatkan berturut turut pada SOFA, APACHE II, dan CSOFA sebesar 84,23%, 8,15%, dan 1,65%, dengan dominan sub variabel hepar (bilirubin). Uji regresi logistik memperlihatkan sub variabel neurologi, kardiovaskular, dan respirasi memberikan hubungan bermakna terhadap mortalitas dengan RO 4,58, 2,24, dan 1,47. Sub variabel lain yang berpengaruh antara lain AKI, sepsis, dan penyakit kronis dengan RO 8,14, 3,89 dan 2,42.Simpulan : CSOFA lebih valid dalam memperkirakan mortalitas pasien di ICU RSUP Sanglah Denpasar, karena mempunyai nilai diskriminasi yang lebih baik dan missing value yang lebih sedikit dibandingkan dengan sistem skoring APACHE II dan SOFA

scholarly journals External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection

2020 ◽  
Author(s):  
Marcus Taylor ◽  
Bartłomiej Szafron ◽  
Glen P Martin ◽  
Udo Abah ◽  
Matthew Smith ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Lung Resection ◽  
External Validation ◽  
Clinical Prediction ◽  
National Guidelines ◽  
Clinical Prediction Models ◽  
Receiver Operating

Abstract OBJECTIVES National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort. METHODS The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance. RESULTS Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality. CONCLUSIONS Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.

scholarly journals Dyspnea and respiratory muscles ultrasound to predict extubation failure

2021 ◽  
pp. 2100002
Author(s):  
Martin Dres ◽  
Thomas Similowski ◽  
Ewan C. Goligher ◽  
Tai Pham ◽  
Liliya Sergenyuk ◽  
...  
Keyword(s):  
Intensive Care ◽  
Respiratory Distress ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Spontaneous Breathing Trial ◽  
Respiratory Muscles ◽  
Extubation Failure ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Receiver Operating

This study investigated dyspnea intensity and respiratory muscles ultrasound early after extubation to predict extubation failure.It was conducted prospectively in two intensive care units in France and Canada. Patients intubated for at least 48 h were studied within 2 h after an extubation following a successful spontaneous breathing trial. Dyspnea was evaluated by the Dyspnea-Visual Analog Scale from 0 to 10 cm (VAS) and the Intensive Care - Respiratory Distress Observational Scale (range 0–10). The ultrasound thickening fraction of the parasternal intercostal and the diaphragm were measured; limb muscle strength was evaluated using the Medical Research Council score (MRC) (range 0–60).Extubation failure occurred in 21 of the 122 enrolled patients (17%). Dyspnea-VAS and Intensive Care - Respiratory Distress Observational scale were higher in patients with extubation failure versus success: 7 (5–9) cm versus 3 (1–5) cm respectively (p<0.001) and 4.4 (2.5–6.5) versus 2.4 (2.1–2.8) respectively (p<0.001). The ratio of intercostal muscle to diaphragm thickening fraction was significantly higher and MRC was lower in patients with failure (0.9 [0.4–3.0] versus 0.3 [0.2–0.5], p<0.001, and 45 [36–50] versus 52 [44–60], p=0.012). The thickening fraction of the intercostal and its ratio to diaphragm thickening showed the highest area under the receiver operating characteristic curves for an early prediction of extubation failure (0.81). Areas under the receiver operating characteristic curves of Dyspnea-VAS and Intensive Care - Respiratory Distress Observational scale reached 0.78 and 0.74 respectively.Respiratory muscle ultrasound and dyspnea measured within 2 h after extubation predict subsequent extubation failure.

scholarly journals Η άλφα1-μικροσφαιρίνη ως δείκτης πρώιμης νεφρικής βλάβης σε βαρέως πάσχοντες σηπτικούς ασθενείς της Μ.Ε.Θ.

2015 ◽  
Author(s):  
Ειρήνη Τερζή
Keyword(s):  
Acute Kidney Injury ◽  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Apache Ii ◽  
Receiver Operating

Μελετήθηκε η συμβολή της άλφα1-μικροσφαιρίνης (alpha1-microglobulin, α1M) - ενός μέλους της οικογένειας των λιποκαλινών, που αποτελεί δείκτη εγγύς νεφροσωληναριακής δυσλειτουργίας - στην πρώιμη διαγνωστική της σχετιζόμενης με την σήψη οξείας νεφρικής βλάβης (acute kidney injury, AKI). Η μελέτη επικεντρώθηκε σε βαρέως πάσχοντες ασθενείς μιας πολυδύναμης Μονάδας Εντατικής Θεραπείας (Μ.Ε.Θ.). Από την προοπτική παρακολούθηση 290 ασθενών που εισήχθησαν για νοσηλεία σε διάστημα ενός έτους, μελετήθηκαν 45 σηπτικοί ασθενείς, εκ των οποίων οι 16 (35.6%) εκδήλωσαν νεφρική ανεπάρκεια. Η α1Μ προσδιορίσθηκε σε δείγματα ούρων από συλλογές ούρων 24ώρου κατά το σηπτικό επεισόδιο και σε συγκεκριμένα χρονικά διαστήματα έκτοτε. Η διαγνωστική ικανότητα του βιοδείκτη εκτιμήθηκε με τον μη παραμετρικό υπολογισμό της περιοχής κάτω από την καμπύλη μίας καμπύλης λειτουργικού χαρακτηριστικού δέκτη (area under the curve (AUC) of the receiver operating characteristic (ROC) curve, AUCROC). Τα επίπεδα της α1Μ ήταν σημαντικά υψηλότερα σε όλους τους σηπτικούς ασθενείς (μέση τιμή επιπέδων σε όλα τα δείγματα στο σηπτικό επεισόδιο 46.02 ± 7.17 mg/l) και παρουσίασαν αυξητική τάση στους ασθενείς που τελικά ανέπτυξαν σηπτική νεφρική ανεπάρκεια. Η AUCROC για την πρόβλεψη της σηπτικής ΑKΙ σύμφωνα με τα επίπεδα της α1M 24 ώρες πριν την εμφάνιση της νεφρικής προσβολής ήταν 0.739 (ευαισθησία 87.5%, ειδικότητα 62.07%, τιμή-όριο 47.9 mg/l). Τα επίπεδα της α1Μ 24 ώρες πριν την σηπτική νεφρική προσβολή, η κρεατινίνη ορού και η βαθμολογία βαρύτητας νόσου κατά APACHE II στο επεισόδιο της σήψης, αναδείχθηκαν ως οι σημαντικότεροι ανεξάρτητοι προγνωστικοί παράγοντες πρόβλεψης της ΑΚΙ. Ο συνδυασμός των ανωτέρω τριών παραμέτρων βελτίωσε την AUCROC της πρόγνωση της AKI σε 0.944. Τα αποτελέσματα υποστηρίζουν την ιδέα πως τα επίπεδα της α1Μ στα ούρα θα μπορούσαν να συμβάλουν στην πρώιμη διάκριση των σηπτικών ασθενών που εξελίσσονται σε ΑΚΙ και μπορεί να αποδειχθούν χρήσιμος βιοδείκτης. Παράλληλα, αναδεικνύουν ως θέμα για περαιτέρω έρευνα την παθογενετική εμπλοκή της α1Μ στην σήψη και στην σηπτική ΑΚΙ.

scholarly journals Akurasi Diagnostik Prokalsitonin Sebagai Penanda Serologis Untuk Membedakan Antara Sepsis Bakterial dan Sepsis Virus

2017 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
Syafri Kamsul Arif ◽  
A. Muh. Farid Wahyuddin ◽  
A. Muh Takdir Musba
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Chi Square ◽  
Cross Sectional ◽  
Roc Area ◽  
Receiver Operating

Prokalsitonin merupakan penandadiagnostik yang baik pada sepsis khususnya sepsis yang disebabkan oleh bakteri. Penelitian ini bertujuan mengetahui sensitivitas, spesifisitas dan akurasi diagnostik prokalsitonin sebagai penandaserologis untuk membedakan antara sepsis bakterial dan virus pada pasien yang dirawat di Intensive Care Unit  (ICU) dan Infection Center (IC) RSUP Dr. Wahidin Sudirohusodo.Penelitian ini merupakan penelitian observasional analitik dengan desain cross sectional menggunakan data sekunder rekam medik pasien sepsis yang dirawat di ICU dan IC RSUP Dr. Wahidin Sudirohusodo, periode 1 Januari 2014 sampai dengan 31 Agustus 2016. Data tersebut dianalisa dengan uji T Independent,  Mann Whitney and Chi-Square dimana terdapat 80 sampel yang memenuhi kriteria inklusi.Dari penelitian ini, terdapat perbedaan yang signifikan dari kadarprokalsitonin antara sepsis bacterial(60.89 ± 73.651 ng/ml) dan sepsis virus (1.12 ± 0.622 ng/ml). Berdasarkan analisis Receiver Operating Characteristic (ROC), area Under the Curve (AUC) dari prokalsitonin adalah 0.841 dengan interval 0.758–0.925 dan signifikansi 95%. Kadar ambang diagnostik terbaik prokalsitonin yang didapatkan pada penelitian ini adalah 1.60 sebagai cut off point (sensitivitas: 82,4%, spesifisitas: 65,2% dan akurasi: 88,7%) untuk membedakan sepsis bakterial dengan sepsis virus. Prokalsitonin memiliki sensitivitas, spesifisitas dan akurasi diagnostik yang baik sebagai pembeda anatar seosis bakterial dan sepsis virus.

scholarly journals Prognostic Accuracy of a Novel Prognostic Score Using a Combination of PCT, DDi, and Lactate Biomarkers in Patients With Sepsis: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Yansong miao ◽  
LiFeng Xing
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Laboratory Data ◽  
Apache Ii ◽  
Cutoff Points ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Background A combination of multiple biomarkers will be more accurate in predicting the mortality of sepsis patients. Herein, we aimed to assess the ability to predict adverse outcomes of a novel scoring system using the combination of PCT, DDi, and lactate (PDLS) in patients with sepsis from the emergency department (ED) of a hospital. Methods The patients’ baseline characteristics, main laboratory data and outcome were collected from the patient's electronic medical record. A receiver operating characteristic curve (ROC) analysis determine the optimal cutoff points for biomarkers PCT, DDi and lactate and establish a PDLS system based on their cutoff points. ROC was used to compare the accuracy of PDLS to Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II scores in predicting short-term mortality in patients with sepsis. Results The analysis cohort included 1001 patients. 117 sepsis patients died in 28 days. An increase in PDLS was associated with higher mortality and adverse events including MV, VD, AICU, and CRRT. PDLS was an independent predictor of 28-day mortality, MV, VD, AICU, and CRRT. The Area Under the Receiver Operating Characteristic curve (AUROC) of PDLS (0.96; Cl=0.94-0.98) was significantly higher than that of SOFA (0.84; Cl=0.80-0.89) and APACHE II (0.84; Cl=0.79-0.88). Conclusion PDLS is an independent prognostic predictor of adverse clinical outcomes for sepsis patients and was superior to other prognostic scores, including SOFA and APACHE II.

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