e-POCT:

Improving Management of Fever in Children in Resource-Poor Settings Through an Electronic

Algorithm Based on

Point-of-Care Tests

Kristina Keitel

D’Acremont et al., NEJM 2014

Unknown Etiology

11.8%

Febrile Diseases: 1005 Tanzanian Children

Viral Diseases57.2%Bacterial

Diseases10.4%

Parasitic Diseases

6.4%

0.9%

Unknown Etiology

11.8%

Viral Diseases57.2%Bacterial

Diseases10.4%

Parasitic Diseases

6.4%

0.9%

Antibioticresistance

Tools at hand

5

Stage 1: Development of e-POCT

Causes of fever

Electronic algorithm

POCTs

6

Clinical predictors

• mRDT• CRP• PCT• Hb• Glucose

e-POCT tool

oximeter

Recommendation for treatment and/or admission

Clinical data

algorithm

Point of Care Tests (POCTs)

Example: cough

IMCI e-POCT

8

• Chest indrawing

• RR• 2-12 months ≥50• >12 months ≥40

• Stridor• (SaO2 <90%)

• SaO2 <90%• Severe respiratory distress (work

of breathing + RR cutoff >97th %ilefor age/T)

Chest indrawing

RR > 75th %ile for age/T CRP≥80

RRSaO2Work of breathing

Step 2: testing in clinical trial in Tanzania

• Randomized controlled non-inferiority trial in health center and hospitals, Dar es Salaam, Tanzania.

• ePOCT versus • ALMANACH (patient level, blocks of 4)• Routine care (clinician level)

• Inclusion:• 2 – 59 months• Fever ≤ 7 days• Axillary T ≥37.5• In catchment area• First consultation

• Exlusion• Primary complaint accident/ poisoning

9

Methodology

Follow-up• Day 3: in person• Day 7: phone if cured at Day 3, otherwise D7• D30: phone

Primary outcome measure: • Proportion of clinical failure by day 7• Difference: 3%

Secondary outcome measures: • Proportion of antibiotic prescription at D0• Secondary hospitalizations/ death by D30

10

Flow diagram

11

Assessed for eligibility (n=15,420) )

Excluded (n=4,047) Not meeting inclusion criteria (n=3,562) Declined to participate (n=238) Had logistic reasons (n=229) Unable to provide consent (n=18)

Allocated to intervention (n=1,608) Received allocated intervention (n=1,593) Did not receive allocated intervention (n=15)-Clinician did not follow algorithm (n=2)-patient withdrew during intervention (n=13)

Allocated to intervention (n=1,607) Received allocated intervention (n=1,595) Did not receive allocated intervention (n=12)-patient withdrew during intervention (n=12)

Allocation

Randomized (n=3,215)

Enrollment

Routine arm (n=548)

Axillary temperature ≥ 37.5C (n=7,810)

Flow diagram

12

mITT (n=1,593)

Lost to follow-up by D7 (n=10 ) Lost to follow-up by D7 (n=14)

mITT (n=1,595)

Analysis

Follow‐Up

Primary outcome

13

Outcome measure ePOCT ALMANACH

Clinical failure by D7, n 33 53

L2FU, n 10 14

Total by Day 7, n/N (%) 43/1593 (2.7) 67/1595 (4.2)

97.5% lower CI for difference: -2.7

Secondary outcome measures

14

Outcome measure n/N (%) ePOCT ALMANACH

Antibiotic prescription at D0 173/1593 (10.3) 463/1595 (29.0)

Secondary admissions byD30 10/1593 (0.6) 11/1595 (0.7)

Death by D30 4/1593(0.2) 6/1595 (0.4)

CRP-patients with cough

15

1593 e-POCT arm

922 cough

722 RR cutoff (75th %ile for age/T)

CRP

16

CRP-patients with cough

CRP (mg/L)

<10 10-39 40-79 ≥80

277 (65%) 115 (27%) 26 (6%) 10 (1%)

0027

Clinical failure by D7

Conclusion

• ePOCT, as an innovative tool, has the potential to improve management of children with fever in resource-poor settings and to increase appropriate use of antibiotics.

• Biomarkers of inflammation can help reducing antibiotic prescription

• Novel biomarkers of inflammation should be evaluated in clinical outcome studies

17

Next steps

• Further testing in population at higher risk for bacterial infections

• Implementation studies

• Assessment of novel biomarkers

18

Swiss TPHValérie d’AcremontBlaise GentonChristian Lengeler

Ifakara Health InsituteFrank KagoroJohn MasimbaTarsis MlaganileZamzam SaidJosephine SamakaHosiana Temba

19

Dar es Salaam City CouncilWilly SanguGrace Magembe

Boston Children’s HospitalRobert HussonRichard MalleyTanvi SharmaRinn Song

University Hospital GenevaAlain Gervaix

MSF, GenevaClothilde Rambaud

Things PrimeTom Routen

FundingSwiss National Science Foundation (R4D program)Harvard UniversityThrasher FoundationBiomérieux