A clinical algorithm for the diagnosis of malaria: results of an evaluation in an area of low endemicity

Chandramohan, Daniel; Carneiro, Ilona; Kavishwar, Abhay; Brugha, Ruairı́; Desai, Vikas K; Greenwood, Brian

doi:10.1046/j.1365-3156.2001.00739.x

Cited by 73 publications

(58 citation statements)

References 8 publications

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“…Many studies have evaluated the feasibility of clinical algorithms in areas of different levels of malaria transmission (Genton et al, 1994;Gomes et al, 1994;Olaleye et al, 1998;Muhe et al, 1999;Bojang et al, 2000;Chandramohan et al, 2001;Anand et al, 2002;Hozhabri et al, 2002;Malik et al, 2005;Mwangi et al, 2005;Mogensen et al, 2006). They differ in the way scores are created, definition of malaria, age group studied, and level of malaria transmission, but they usually agree that, although a clinical algorithm can improve identification of a case of malaria, a Giemsa-stained blood film reduces over-treatment and wastage of antimalarial drug during this era of increasing cost and widespread increase in parasite resistance to many available drugs (Chandramohan et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

Usefulness of clinical algorithm as screening process to detected malaria in low-to-moderate transmission areas of scarce health related resources

Périssé

Strickland²

2008

Acta Tropica

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Introduction-In areas of low-to-moderate risk of malaria transmission, the World Health Organization recommends parasitic confirmation before treatment. Such areas have usually low budget for health care and malaria diagnosis is mostly based on clinical assumption. Algorithms have been developed to improve health care providers' identification of clinical malaria and could be used as screening to reduce the number of individuals requiring parasitic confirmation before treating.Methods-Prospective clinical and parasitological data were collected from inhabitants of four villages from March 1984 through March 1985. Symptoms and signs recorded by physicians were used in multivariate models to test the best predictors of malaria. Sensitivity and specificity were calculated for various cut-offs of scores and compared to clinical diagnosis.Results-A total of 8.941 individuals were evaluated during the 1-year period of data collection. The overall prevalence of malaria parasitemia was 19.7% (n = 1762). Of the 4280 people evaluated during the high season period, 24% (n = 1024) presented any parasitemia, 55.3% (566/1024) due to Plasmodium falciparum. The final clinical algorithm included history of fever, rigors, headache, absence of myalgia, backache or cough, nausea or vomiting, and splenomegaly on examination as predictable variables. At a cut-off score of 2.0, the sensitivity of the algorithm was higher for the entire sample (57% vs. 43%), for high season period (70% vs. 53%), for children less than 6 years of age (59% vs. 40%), for individuals with parasitemia due to P. falciparum (65% vs. 48%), and for high P. falciparum parasitemic individuals at high season (84% vs. 68%). However, specificity was usually lower unless a higher cut off was used, in which case the gain in sensitivity by using the algorithm was reduced.Conclusion-In low-to-moderate transmission areas in which health related resources are scarce, a clinical algorithm increases the identification of real cases of malaria and could be used as screening for further parasitic identification.

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Section: Discussionmentioning

confidence: 99%

Usefulness of clinical algorithm as screening process to detected malaria in low-to-moderate transmission areas of scarce health related resources

Périssé

Strickland²

2008

Acta Tropica

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“…The extent of liver involvement varies from a minimal abnormality in liver function tests to hepatic encephalopathy. [13][14][15] Chawla et al studied 31 patients, of whom 14 had serum bilirubin>10mg%, with predominantly conjugated hyperbilirubinaemia. 16 They attributed these elevated serum bilirubin levels to intravascular haemolysis and associated renal failure, leading to decreased excretion of bilirubin.…”

Section: Discussionmentioning

confidence: 99%

Study of clinical presentation of malaria and the associated liver profile changes in various species of plasmodia

Biradar

Holyachi

2016

Int J Res Med Sci

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“…In [11], Chandramohan et al, (2001) developed and evaluated a clinical algorithm. This development of the algorithm came up after a study on 1945 children and 2885 adults who suffered from fever in a hospital outpatients clinic in India.…”

Section: Review Of Related Workmentioning

confidence: 99%

Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)

Owoseni¹,

Ogundahunsi²

2016

IJIEEB

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Abstract-Malaria is a deadly disease that claims yearly lives of millions in Africa, and other endemic continents. The prevalence of malaria in these endemic regions is majorly attached to the lack of competent medical experts who are capable of providing medical care for the affected victims. This study considers developing a mobile based fuzzy expert system that could assist in diagnosing malaria. The fuzzification of crisp inputs by the system was carried out using an inter-valued and triangular membership functions while the deffuzification of the inference engine outputs was performed by weighted average method. Root sum square method of drawing inferences has been employed while the whole development has been achieved with the help of Java 2 Micro Edition of Java. This expert system executes on the readily available mobile devices of the patients. This fuzzy system was finally evaluated and confirmed effective in providing a human-expert like diagnosis.

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A clinical algorithm for the diagnosis of malaria: results of an evaluation in an area of low endemicity

Cited by 73 publications

References 8 publications

Usefulness of clinical algorithm as screening process to detected malaria in low-to-moderate transmission areas of scarce health related resources

Usefulness of clinical algorithm as screening process to detected malaria in low-to-moderate transmission areas of scarce health related resources

Study of clinical presentation of malaria and the associated liver profile changes in various species of plasmodia

Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)

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