Polish
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Risk Management and Healthcare Policy 2013

Clinical risk-scoring algorithm to forecast scrub typhus severity.

Tylko zarejestrowani użytkownicy mogą tłumaczyć artykuły
Zaloguj się Zarejestruj się
Link zostanie zapisany w schowku
Pamornsri Sriwongpan
Pornsuda Krittigamas
Hutsaya Tantipong
Jayanton Patumanond
Chamaiporn Tawichasri
Sirianong Namwongprom

Słowa kluczowe

Abstrakcyjny

OBJECTIVE

To develop a simple risk-scoring system to forecast scrub typhus severity.

METHODS

Seven years' retrospective data of patients diagnosed with scrub typhus from two university-affiliated hospitals in the north of Thailand were analyzed. Patients were categorized into three severity groups: nonsevere, severe, and dead. Predictors for severity were analyzed under multivariable ordinal continuation ratio logistic regression. Significant coefficients were transformed into item score and summed to total scores.

RESULTS

Predictors of scrub typhus severity were age >15 years, (odds ratio [OR] =4.09), pulse rate >100/minute (OR 3.19), crepitation (OR 2.97), serum aspartate aminotransferase >160 IU/L (OR 2.89), serum albumin ≤3.0 g/dL (OR 4.69), and serum creatinine >1.4 mg/dL (OR 8.19). The scores which ranged from 0 to 16, classified patients into three risk levels: non-severe (score ≤5, n=278, 52.8%), severe (score 6-9, n=143, 27.2%), and fatal (score ≥10, n=105, 20.0%). Exact severity classification was obtained in 68.3% of cases. Underestimations of 5.9% and overestimations of 25.8% were clinically acceptable.

CONCLUSIONS

The derived scrub typhus severity score classified patients into their severity levels with high levels of prediction, with clinically acceptable under- and overestimations. This classification may assist clinicians in patient prognostication, investigation, and management. The scoring algorithm should be validated by independent data before adoption into routine clinical practice.

Dołącz do naszej strony
na Facebooku

Najbardziej kompletna baza danych ziół leczniczych poparta naukowo

  • Działa w 55 językach
  • Ziołowe leki poparte nauką
  • Rozpoznawanie ziół na podstawie obrazu
  • Interaktywna mapa GPS - oznacz zioła na miejscu (wkrótce)
  • Przeczytaj publikacje naukowe związane z Twoim wyszukiwaniem
  • Szukaj ziół leczniczych po ich działaniu
  • Uporządkuj swoje zainteresowania i bądź na bieżąco z nowościami, badaniami klinicznymi i patentami

Wpisz objaw lub chorobę i przeczytaj o ziołach, które mogą pomóc, wpisz zioło i zobacz choroby i objawy, na które są stosowane.
* Wszystkie informacje oparte są na opublikowanych badaniach naukowych

Google Play badgeApp Store badge