A Ratsimbasoa

AJ de Langen

B Ngasala

BV Mendelow

C Beadle

C Briggs

C Wongsrichanalai

CJ Palmer

CS Scott

H Reyburn

H Tagbor

HM Kodisinghe

LM Erhart

ME van Wolfswinkel

ML McMorrow

N Zerpa

NM Laban

PL Bhandari

RN Maina

S Bakhubaira

SA Harvey

SW Lee

T Endeshaw

T Hanscheid

T Hänscheid

TK Vo

TW Mwangi

Scientific Reports

AbstractNowadays, the gold standard method for malaria diagnosis is a staining of thick and thin blood film examined by expert laboratorists. It requires well-trained laboratorists, which is a time consuming task and is un-automated protocol. For this study, Maladiag Software was developed to predict malaria infection in suspected malaria patients. The demographic data of patients, examination for malaria parasites and complete blood count (CBC) profiles were analyzed. Binary logistic regression was used to create the equation for the malaria diagnosis. The diagnostic parameters of the equation were tested on 4,985 samples (703 infected and 4,282 control samples). The equation indicated 81.2% sensitivity and 80.3% specificity for predicting infection of malaria. The positive likelihood and negative likelihood ratio were 4.12 (95% CI = 4.01–4.23) and 0.23 (95% CI = 0.22–0.25), respectively. This parameter also had odds ratios (P value &lt; 0.0001, OR = 17.6, 95% CI = 16.0–19.3). The equation can predict malaria infection after adjust for age, gender, nationality, monocyte (%), platelet count, neutrophil (%), lymphocyte (%) and the RBC count of patients. The diagnostic accuracy was 0.877 (Area under curve, AUC) (95% CI = 0.871–0.883). The system, when used in combination with other clinical and microscopy methods, might improve malaria diagnoses and enhance prompt treatment.</jats:p

A diagnostic tool for malaria based on computer software

Abstract

Similar works

Full text

Available Versions

Crossref